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Understanding the Relation Between Maximum-Entropy Inverse Reinforcement Learning and Behaviour Cloning
Inverse Reinforcement Learning Behaviour Cloning f-divergence distribution matching
In many settings, it is desirable to learn decision-making and control policies through learning or from expert demonstrations. The most common approaches under this framework are Behaviour Cloning (BC), and Inverse Reinforcement Learning (IRL). Recent methods for IRL have demonstrated the capacity to learn effective policies with access to a very limited set of demonstrations, a scenario in which BC methods often fail. Unfortunately, directly comparing the algorithms for these methods does not provide adequate intuition for understanding this difference in performance. This is the motivating factor for our work. We begin by presenting $f$-MAX, a generalization of AIRL (Fu et al., 2018), a state-of-the-art IRL method. $f$-MAX provides grounds for more directly comparing the objectives for LfD. We demonstrate that $f$-MAX, and by inheritance AIRL, is a subset of the cost-regularized IRL framework laid out by Ho & Ermon (2016). We conclude by empirically evaluating the factors of difference between various LfD objectives in the continuous control domain.
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UniGuard: Towards Universal Safety Guardrails for Jailbreak Attacks on Multimodal Large Language Models
Safety Guardrail Social Media Multimodality Multimodal LLMs
Multimodal large language models (MLLMs) have revolutionized vision-language understanding but remain vulnerable to multimodal jailbreak attacks, where adversarial inputs are meticulously crafted to elicit harmful or inappropriate responses. We propose UniGuard, a novel multimodal safety guardrail that jointly considers the unimodal and cross-modal harmful signals. UniGuard trains a multimodal guardrail to minimize the likelihood of generating harmful responses in a toxic corpus. The guardrail can be seamlessly applied to any input prompt during inference with minimal computational costs. Extensive experiments demonstrate the generalizability of UniGuard across multiple modalities, attack strategies, and multiple state-of-the-art MLLMs, including LLaVA, Gemini Pro, GPT-4o, MiniGPT-4, and InstructBLIP. Notably, this robust defense mechanism maintains the models' overall vision-language understanding capabilities. Our code is available at https://anonymous.4open.science/r/UniGuard/README.md.
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Optimal Transport Graph Neural Networks
graph neural networks optimal transport molecular representations molecular property prediction
Current graph neural network (GNN) architectures naively average or sum node embeddings into an aggregated graph representation---potentially losing structural or semantic information. We here introduce OT-GNN, a model that computes graph embeddings using parametric prototypes that highlight key facets of different graph aspects. Towards this goal, we are (to our knowledge) the first to successfully combine optimal transport with parametric graph models. Graph representations are obtained from Wasserstein distances between the set of GNN node embeddings and "prototype" point clouds as free parameters. We theoretically prove that, unlike traditional sum aggregation, our function class on point clouds satisfies a fundamental universal approximation theorem. Empirically, we address an inherent collapse optimization issue by proposing a noise contrastive regularizer to steer the model towards truly exploiting the optimal transport geometry. Finally, we consistently report better generalization performance on several molecular property prediction tasks, while exhibiting smoother graph representations.
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Theoretical bounds on estimation error for meta-learning
meta learning few-shot minimax risk lower bounds learning theory
Machine learning models have traditionally been developed under the assumption that the training and test distributions match exactly. However, recent success in few-shot learning and related problems are encouraging signs that these models can be adapted to more realistic settings where train and test distributions differ. Unfortunately, there is severely limited theoretical support for these algorithms and little is known about the difficulty of these problems. In this work, we provide novel information-theoretic lower-bounds on minimax rates of convergence for algorithms that are trained on data from multiple sources and tested on novel data. Our bounds depend intuitively on the information shared between sources of data, and characterize the difficulty of learning in this setting for arbitrary algorithms. We demonstrate these bounds on a hierarchical Bayesian model of meta-learning, computing both upper and lower bounds on parameter estimation via maximum-a-posteriori inference.
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Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Learning
COVID-19 Diagnosis COVID-19 Prognosis GCN
Coronavirus disease 2019 (COVID-19), the pandemic that is spreading fast globally, has caused over 34 million confirmed cases. Apart from the reverse transcription polymerase chain reaction (RT-PCR), the chest computed tomography (CT) is viewed as a standard and effective tool for disease diagnosis and progression monitoring. We propose a diagnosis and prognosis model based on graph convolutional networks (GCNs). The chest CT scan of a patient, typically involving hundreds of sectional images in sequential order, is formulated as a densely connected weighted graph. A novel distance aware pooling is proposed to abstract the node information hierarchically, which is robust and efficient for such densely connected graphs. Our method, combining GCNs and distance aware pooling, can integrate the information from all slices in the chest CT scans for optimal decision making, which leads to the state-of-the-art accuracy in the COVID-19 diagnosis and prognosis. With less than 1% number of total parameters in the baseline 3D ResNet model, our method achieves 94.8% accuracy for diagnosis. It has a 2.4% improvement compared with the baseline model on the same dataset. In addition, we can localize the most informative slices with disease lesions for COVID-19 within a large sequence of chest CT images. The proposed model can produce visual explanations for the diagnosis and prognosis, making the decision more transparent and explainable, while RT-PCR only leads to the test result with no prognosis information. The prognosis analysis can help hospitals or clinical centers designate medical resources more efficiently and better support clinicians to determine the proper clinical treatment.
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Prototypical Metric Transfer Learning for Continuous Speech Keyword Spotting With Limited Training Data
Audio keyword detection prototypical Metric Loss Few-shot Transfer Learning
Continuous Speech Keyword Spotting (CSKS) is the problem of spotting keywords in recorded conversations, when a small number of instances of keywords are available in training data. Unlike the more common Keyword Spotting, where an algorithm needs to detect lone keywords or short phrases like "Alexa”, “Cortana", “Hi Alexa!”, “`Whatsup Octavia?” etc. in speech, CSKS needs to filter out embedded words from a continuous flow of speech, ie. spot “Anna” and “github” in “I know a developer named Anna who can look into this github issue.” Apart from the issue of limited training data availability, CSKS is an extremely imbalanced classification problem. We address the limitations of simple keyword spotting baselines for both aforementioned challenges by using a novel combination of loss functions (Prototypical networks’ loss and metric loss) and transfer learning. Our method improves F1 score by over 10%.
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GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing
text-to-sql semantic parsing pre-training nlp
We present GraPPa, an effective pre-training approach for table semantic parsing that learns a compositional inductive bias in the joint representations of textual and tabular data. We construct synthetic question-SQL pairs over high-quality tables via a synchronous context-free grammar (SCFG). We pre-train our model on the synthetic data to inject important structural properties commonly found in semantic parsing into the pre-training language model. To maintain the model's ability to represent real-world data, we also include masked language modeling (MLM) on several existing table-related datasets to regularize our pre-training process. Our proposed pre-training strategy is much data-efficient. When incorporated with strong base semantic parsers, GraPPa achieves new state-of-the-art results on four popular fully supervised and weakly supervised table semantic parsing tasks.
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Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
meta-reinforcement learning
Deep reinforcement learning algorithms require large amounts of experience to learn an individual task. While in principle meta-reinforcement learning (meta-RL) algorithms enable agents to learn new skills from small amounts of experience, several major challenges preclude their practicality. Current methods rely heavily on on-policy experience, limiting their sample efficiency. They also lack mechanisms to reason about task uncertainty when adapting to new tasks, limiting their effectiveness in sparse reward problems. In this paper, we address these challenges by developing an off-policy meta-RL algorithm that disentangles task inference and control. In our approach, we perform online probabilistic filtering of latent task variables to infer how to solve a new task from small amounts of experience. This probabilistic interpretation enables posterior sampling for structured and efficient exploration. We demonstrate how to integrate these task variables with off-policy RL algorithms to achieve both meta-training and adaptation efficiency. Our method outperforms prior algorithms in sample efficiency by 20-100X as well as in asymptotic performance on several meta-RL benchmarks.
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Two steps at a time --- taking GAN training in stride with Tseng's method
steps time gan training stride tseng training minimax problems generative adversarial networks gans methods
Motivated by the training of Generative Adversarial Networks (GANs), we study methods for solving minimax problems with additional nonsmooth regularizers.
We do so by employing \emph{monotone operator} theory, in particular the \emph{Forward-Backward-Forward (FBF)} method, which avoids the known issue of limit cycling by correcting each update by a second gradient evaluation.
Furthermore, we propose a seemingly new scheme which recycles old gradients to mitigate the additional computational cost.
In doing so we rediscover a known method, related to \emph{Optimistic Gradient Descent Ascent (OGDA)}.
For both schemes we prove novel convergence rates for convex-concave minimax problems via a unifying approach. The derived error bounds are in terms of the gap function for the ergodic iterates.
For the deterministic and the stochastic problem we show a convergence rate of $\mathcal{O}(\nicefrac{1}{k})$ and $\mathcal{O}(\nicefrac{1}{\sqrt{k}})$, respectively.
We complement our theoretical results with empirical improvements in the training of Wasserstein GANs on the CIFAR10 dataset.
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Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients
SG-MCMC Non-uniform weight
Common Stochastic Gradient MCMC methods approximate gradients by stochastic ones via uniformly subsampled data points. A non-uniform subsampling scheme, however, can reduce the variance introduced by the stochastic approximation and make the sampling of a target distribution more accurate. For this purpose, an exponentially weighted stochastic gradient approach (EWSG) is developed to match the transition kernel of a non-uniform-SG-MCMC method with that of a batch-gradient-MCMC method. If needed to be put in the importance sampling (IS) category, EWSG can be viewed as a way to extend the IS+SG approach successful for optimization to the sampling setup. EWSG works for a range of MCMC methods, and a demonstration on Stochastic-Gradient 2nd-order Langevin is provided. In our practical implementation of EWSG, the non-uniform subsampling is performed efficiently via a Metropolis-Hasting chain on the data index, which is coupled to the sampling algorithm. The fact that our method has reduced local variance with high probability is theoretically analyzed. A non-asymptotic global error analysis is also presented. As a practical implementation contains hyperparameters, numerical experiments based on both synthetic and real world data sets are provided, to both demonstrate the empirical performances and recommend hyperparameter choices. Notably, while statistical accuracy has improved, the speed of convergence, with appropriately chosen hyper-parameters, was empirically observed to be at least comparable to the uniform version, which renders EWSG a practically useful alternative to common variance reduction treatments.
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Time Series Counterfactual Inference with Hidden Confounders
Time Series Analysis Counterfactual Inference Differential Equations.
We present augmented counterfactual ordinary differential equations (ACODEs), a new approach to counterfactual inference on time series data with a focus on healthcare applications. ACODEs model interventions in continuous time with differential equations, augmented by auxiliary confounding variables to reduce inference bias. Experiments on tumor growth simulation and sepsis patient treatment response show that ACODEs outperform other methods like counterfactual Gaussian processes, recurrent marginal structural networks, and time series deconfounders in the accuracy of counterfactual inference. The learned auxiliary variables also reveal new insights into causal interventions and hidden confounders.
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Safe Reinforcement Learning with Natural Language Constraints
Safe reinforcement learning Language grounding
In this paper, we tackle the problem of learning control policies for tasks when provided with constraints in natural language. In contrast to instruction following, language here is used not to specify goals, but rather to describe situations that an agent must avoid during its exploration of the environment. Specifying constraints in natural language also differs from the predominant paradigm in safe reinforcement learning, where safety criteria are enforced by hand-defined cost functions. While natural language allows for easy and flexible specification of safety constraints and budget limitations, its ambiguous nature presents a challenge when mapping these specifications into representations that can be used by techniques for safe reinforcement learning. To address this, we develop a model that contains two components: (1) a constraint interpreter to encode natural language constraints into vector representations capturing spatial and temporal information on forbidden states, and (2) a policy network that uses these representations to output a policy with minimal constraint violations. Our model is end-to-end differentiable and we train it using a recently proposed algorithm for constrained policy optimization. To empirically demonstrate the effectiveness of our approach, we create a new benchmark task for autonomous navigation with crowd-sourced free-form text specifying three different types of constraints. Our method outperforms several baselines by achieving 6-7 times higher returns and 76% fewer constraint violations on average. Dataset and code to reproduce our experiments are available at https://sites.google.com/view/polco-hazard-world/.
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A Multi-Modal and Multitask Benchmark in the Clinical Domain
multi-modal multitask machine learning in healthcare benchmark
Healthcare represents one of the most promising application areas for machine learning algorithms, including modern methods based on deep learning. Modern deep learning algorithms perform best on large datasets and on unstructured modalities such as text or image data; advances in deep learning have often been driven by the availability of such large datasets. Here, we introduce Multi-Modal Multitask MIMIC-III (M3) — a dataset and benchmark for evaluating machine learning algorithms in the healthcare domain. This dataset contains multi-modal patient data collected from intensive care units — including physiological time series, clinical notes, ECG waveforms, and tabular inputs — and defines six clinical tasks — including predicting mortality, decompensation, readmission, and other outcomes — which serve as benchmarks for comparing algorithms. We introduce new multi-modal and multitask models for this dataset, and show that they outperform previous state-of-the-art results that only rely on a subset of all tasks and modalities. This highlights the potential of multitask and multi-modal learning to improve the performance of algorithms in the healthcare domain. More generally, we envision M3 as a general resource that will help accelerate research in applying machine learning to healthcare.
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Learning Sparse Latent Representations with the Deep Copula Information Bottleneck
Information Bottleneck Deep Information Bottleneck Deep Variational Information Bottleneck Variational Autoencoder Sparsity Disentanglement Interpretability Copula Mutual Information
Deep latent variable models are powerful tools for representation learning. In this paper, we adopt the deep information bottleneck model, identify its shortcomings and propose a model that circumvents them. To this end, we apply a copula transformation which, by restoring the invariance properties of the information bottleneck method, leads to disentanglement of the features in the latent space. Building on that, we show how this transformation translates to sparsity of the latent space in the new model. We evaluate our method on artificial and real data.
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On Graph Neural Networks versus Graph-Augmented MLPs
Graph Neural Networks expressive power feature propagation rooted graphs attributed walks community detection depth separation
From the perspectives of expressive power and learning, this work compares multi-layer Graph Neural Networks (GNNs) with a simplified alternative that we call Graph-Augmented Multi-Layer Perceptrons (GA-MLPs), which first augments node features with certain multi-hop operators on the graph and then applies learnable node-wise functions. From the perspective of graph isomorphism testing, we show both theoretically and numerically that GA-MLPs with suitable operators can distinguish almost all non-isomorphic graphs, just like the Weisfeiler-Lehman (WL) test and GNNs. However, by viewing them as node-level functions and examining the equivalence classes they induce on rooted graphs, we prove a separation in expressive power between GA-MLPs and GNNs that grows exponentially in depth. In particular, unlike GNNs, GA-MLPs are unable to count the number of attributed walks. We also demonstrate via community detection experiments that GA-MLPs can be limited by their choice of operator family, whereas GNNs have higher flexibility in learning.
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CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding
data augmentation natural language understanding consistency training contrastive learning
Data augmentation has been demonstrated as an effective strategy for improving model generalization and data efficiency. However, due to the discrete nature of natural language, designing label-preserving transformations for text data tends to be more challenging. In this paper, we propose a novel data augmentation frame-work dubbed CoDA, which synthesizes diverse and informative augmented examples by integrating multiple transformations organically. Moreover, a contrastive regularization is introduced to capture the global relationship among all the data samples. A momentum encoder along with a memory bank is further leveraged to better estimate the contrastive loss. To verify the effectiveness of the proposed framework, we apply CoDA to Transformer-based models on a wide range of natural language understanding tasks. On the GLUE benchmark, CoDA gives rise to an average improvement of 2.2%while applied to the Roberta-large model. More importantly, it consistently exhibits stronger results relative to several competitive data augmentation and adversarial training baselines (including the low-resource settings). Extensive experiments show that the proposed contrastive objective can be flexibly combined with various data augmentation approaches to further boost their performance, highlighting the wide applicability of the CoDA framework.
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Achieving Exact Federated Unlearning with Improved Post-Unlearning Performance
Exact Federated Unlearning Improved Post-Unlearning Performance Multi-Models Training
Federated learning is a machine learning paradigm that allows multiple clients to train aggregated model via sharing model updates to a central server without sharing their data. Even though the data is not shared, it can indirectly influence the aggregated model via the shared model updates. In many real-life scenarios, we need to completely remove a client's influence (unlearning) from the aggregated model, such as competitive clients who want to remove their influence from the aggregated model (e.g., large language models (LLMs) fine-tuned collaboratively by multiple clients for a specific downstream task) after leaving the coalition to ensure other clients do not benefit from their contributions. The influence removal is also needed when the adversarial client negatively affects the aggregated model. Though the aggregated model can be retrained from scratch to ensure exact unlearning (completely removing the client's influence from the aggregated model), it performs poorly just after the unlearning, which is undesirable during deployment. To overcome this challenge, this paper proposes federated unlearning algorithms that ensure exact unlearning while achieving better performance post-unlearning. The proposed algorithms are problem-agnostic, making them applicable across various domains. Our experimental results further validate the effectiveness of the proposed federated unlearning algorithms in fine-tuning LLMs and performing vision tasks within a federated learning framework using real-world datasets.
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Parrot: Data-Driven Behavioral Priors for Reinforcement Learning
reinforcement learning imitation learning
Reinforcement learning provides a general framework for flexible decision making and control, but requires extensive data collection for each new task that an agent needs to learn. In other machine learning fields, such as natural language processing or computer vision, pre-training on large, previously collected datasets to bootstrap learning for new tasks has emerged as a powerful paradigm to reduce data requirements when learning a new task. In this paper, we ask the following question: how can we enable similarly useful pre-training for RL agents? We propose a method for pre-training behavioral priors that can capture complex input-output relationships observed in successful trials from a wide range of previously seen tasks, and we show how this learned prior can be used for rapidly learning new tasks without impeding the RL agent's ability to try out novel behaviors. We demonstrate the effectiveness of our approach in challenging robotic manipulation domains involving image observations and sparse reward functions, where our method outperforms prior works by a substantial margin. Additional materials can be found on our project website: https://sites.google.com/view/parrot-rl
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Optimal transport maps for distribution preserving operations on latent spaces of Generative Models
Generative Models GANs latent space operations optimal transport
Generative models such as Variational Auto Encoders (VAEs) and Generative Adversarial Networks (GANs) are typically trained for a fixed prior distribution in the latent space, such as uniform or Gaussian.
After a trained model is obtained, one can sample the Generator in various forms for exploration and understanding, such as interpolating between two samples, sampling in the vicinity of a sample or exploring differences between a pair of samples applied to a third sample.
In this paper, we show that the latent space operations used in the literature so far induce a distribution mismatch between the resulting outputs and the prior distribution the model was trained on. To address this, we propose to use distribution matching transport maps to ensure that such latent space operations preserve the prior distribution, while minimally modifying the original operation.
Our experimental results validate that the proposed operations give higher quality samples compared to the original operations.
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Reproducibility and Stability Analysis in Metric-Based Few-Shot Learning
reproducibility few-shot machine learning statistics
We propose a study of the stability of several few-shot learning algorithms subject to variations in the hyper-parameters and optimization schemes while controlling the random seed. We propose a methodology for testing for statistical differences in model performances under several replications. To study this specific design, we attempt to reproduce results from three prominent papers: Matching Nets, Prototypical Networks, and TADAM. We analyze on the miniImagenet dataset on the standard classification task in the 5-ways, 5-shots learning setting at test time. We find that the selected implementations exhibit stability across random seed, and repeats.
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Improving Learning to Branch via Reinforcement Learning
Mixed Integer Programming Branching and Bound Strong Branching Reinforcement Learning Evolution Strategy Novelty Search
Branch-and-Bound~(B\&B) is a general and widely used algorithm paradigm for solving Mixed Integer Programming~(MIP).
Recently there is a surge of interest in designing learning-based branching policies as a fast approximation of strong branching, a human-designed heuristic. In this work, we argue strong branching is not a good expert to imitate for its poor decision quality when turning off its side effects in solving linear programming. To obtain more effective and non-myopic policies than a local heuristic, we formulate the branching process in MIP as reinforcement learning~(RL) and design a policy characterization for the B\&B process to improve our agent by novelty search evolutionary strategy. Across a range of NP-hard problems, our trained RL agent significantly outperforms expert-designed branching rules and the state-of-the-art learning-based branching methods in terms of both speed and effectiveness. Our results suggest that with carefully designed policy networks and learning algorithms, reinforcement learning has the potential to advance algorithms for solving MIPs.
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Online Limited Memory Neural-Linear Bandits
bandits exploration program algorithm online limited memory limited memory problems representation learning important role representation power
We study neural-linear bandits for solving problems where both exploration and representation learning play an important role. Neural-linear bandits leverage the representation power of deep neural networks and combine it with efficient exploration mechanisms, designed for linear contextual bandits, on top of the last hidden layer. Since the representation is optimized during learning, information regarding exploration with “old” features is lost. We propose the first limited memory neural- linear bandit that is resilient to this catastrophic forgetting phenomenon by solving a semi-definite program. We then approximate the semi-definite program using stochastic gradient descent to make the algorithm practical and adjusted for online usage. We perform simulations on a variety of data sets, including regression, classification, and sentiment analysis. In addition, we evaluate our algorithm in a challenging uplink rate-control application. The bandit controls the transmission rates of data segments over cellular links to achieve optimal throughput. We observe that our algorithm achieves superior performance and shows resilience to catastrophic forgetting.
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Ringing ReLUs: Harmonic Distortion Analysis of Nonlinear Feedforward Networks
deep learning theory loss landscape harmonic distortion analysis network trainability
In this paper, we apply harmonic distortion analysis to understand the effect of nonlinearities in the spectral domain. Each nonlinear layer creates higher-frequency harmonics, which we call "blueshift", whose magnitude increases with network depth, thereby increasing the “roughness” of the output landscape. Unlike differential models (such as vanishing gradients, sharpness), this provides a more global view of how network architectures behave across larger areas of their parameter domain. For example, the model predicts that residual connections are able to counter the effect by dampening corresponding higher frequency modes. We empirically verify the connection between blueshift and architectural choices, and provide evidence for a connection with trainability.
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Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models
variational inference state-space models amortized inference recurrent networks
Amortised inference enables scalable learning of sequential latent-variable models (LVMs) with the evidence lower bound (ELBO). In this setting, variational posteriors are often only partially conditioned. While the true posteriors depend, e.g., on the entire sequence of observations, approximate posteriors are only informed by past observations. This mimics the Bayesian filter---a mixture of smoothing posteriors. Yet, we show that the ELBO objective forces partially-conditioned amortised posteriors to approximate products of smoothing posteriors instead. Consequently, the learned generative model is compromised. We demonstrate these theoretical findings in three scenarios: traffic flow, handwritten digits, and aerial vehicle dynamics. Using fully-conditioned approximate posteriors, performance improves in terms of generative modelling and multi-step prediction.
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Enhancing LLM Robustness to Perturbed Instructions: An Empirical Study
LLMs robustness
Large Language Models (LLMs) are highly vulnerable to input perturbations, as even a small prompt change may result in a substantially different output. Existing methods to enhance LLM robustness are primarily focused on perturbed data samples, whereas improving resiliency to perturbations of task-level instructions has remained relatively underexplored. In this work, we focus on character- and word-level edits of task-specific instructions, which substantially degrade downstream performance. We experiment with a variety of techniques to enhance the robustness of LLMs, including self-denoising and representation alignment, testing different models (Llama 3 and Flan-T5), datasets (CoLa, QNLI, SST-2) and instructions (both task-oriented and role-oriented). We find that, on average, self-denoising—whether performed by a frozen LLM or a fine-tuned model—achieves substantially higher performance gains than alternative strategies, including more complex baselines such as ensembling and supervised methods.
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Robustness to Pruning Predicts Generalization in Deep Neural Networks
Generalization Pruning Generalization Measures
Why over-parameterized neural networks generalize as well as they do is a central concern of theoretical analysis in machine learning today. Following Occam's razor, it has long been suggested that simpler networks generalize better than more complex ones. Successfully quantifying this principle has proved difficult given that many measures of simplicity, such as parameter norms, grow with the size of the network and thus fail to capture the observation that larger networks tend to generalize better in practice.
In this paper, we introduce a new, theoretically motivated measure of a network's simplicity: the smallest fraction of the network's parameters that can be kept while pruning without adversely affecting its training loss. We show that this measure is highly predictive of a model's generalization performance across a large set of convolutional networks trained on CIFAR-10. Lastly, we study the mutual information between the predictions of our new measure and strong existing measures based on models' margin, flatness of minima and optimization speed. We show that our new measure is similar to -- but more predictive than -- existing flatness-based measures.
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Towards Resolving the Implicit Bias of Gradient Descent for Matrix Factorization: Greedy Low-Rank Learning
matrix factorization gradient descent implicit regularization implicit bias
Matrix factorization is a simple and natural test-bed to investigate the implicit regularization of gradient descent. Gunasekar et al. (2017) conjectured that gradient flow with infinitesimal initialization converges to the solution that minimizes the nuclear norm, but a series of recent papers argued that the language of norm minimization is not sufficient to give a full characterization for the implicit regularization. In this work, we provide theoretical and empirical evidence that for depth-2 matrix factorization, gradient flow with infinitesimal initialization is mathematically equivalent to a simple heuristic rank minimization algorithm, Greedy Low-Rank Learning, under some reasonable assumptions. This generalizes the rank minimization view from previous works to a much broader setting and enables us to construct counter-examples to refute the conjecture from Gunasekar et al. (2017). We also extend the results to the case where depth >= 3, and we show that the benefit of being deeper is that the above convergence has a much weaker dependence over initialization magnitude so that this rank minimization is more likely to take effect for initialization with practical scale.
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Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Time
Bayesian networks robust statistics learning theory
We study the problem of learning Bayesian networks where an $\epsilon$-fraction of the samples are adversarially corrupted. We focus on the fully-observable case where the underlying graph structure is known. In this work, we present the first nearly-linear time algorithm for this problem with a dimension-independent error guarantee. Previous robust algorithms with comparable error guarantees are slower by at least a factor of $(d/\epsilon)$, where $d$ is the number of variables in the Bayesian network and $\epsilon$ is the fraction of corrupted samples.
Our algorithm and analysis are considerably simpler than those in previous work. We achieve this by establishing a direct connection between robust learning of Bayesian networks and robust mean estimation. As a subroutine in our algorithm, we develop a robust mean estimation algorithm whose runtime is nearly-linear in the number of nonzeros in the input samples, which may be of independent interest.
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Uncertainty for deep image classifiers on out of distribution data.
uncertainty confidence out of distribution outlier exposure classification
In addition to achieving high accuracy, in many applications, it is important to estimate the probability that a model prediction is correct. Predictive uncertainty is particularly important on out of distribution (OOD) data where accuracy degrades. However, models are typically overconfident, and model calibration on OOD data remains a challenge. In this paper we propose a simple post hoc calibration method that significantly improves on benchmark results [Ovadia et al 2019] on a wide range of corrupted data. Our method uses outlier exposure to properly calibrate the model probabilities.
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Rethinking Embedding Coupling in Pre-trained Language Models
natural language processing transfer learning efficiency pre-training
We re-evaluate the standard practice of sharing weights between input and output embeddings in state-of-the-art pre-trained language models. We show that decoupled embeddings provide increased modeling flexibility, allowing us to significantly improve the efficiency of parameter allocation in the input embedding of multilingual models. By reallocating the input embedding parameters in the Transformer layers, we achieve dramatically better performance on standard natural language understanding tasks with the same number of parameters during fine-tuning. We also show that allocating additional capacity to the output embedding provides benefits to the model that persist through the fine-tuning stage even though the output embedding is discarded after pre-training. Our analysis shows that larger output embeddings prevent the model's last layers from overspecializing to the pre-training task and encourage Transformer representations to be more general and more transferable to other tasks and languages. Harnessing these findings, we are able to train models that achieve strong performance on the XTREME benchmark without increasing the number of parameters at the fine-tuning stage.
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Invariant Causal Representation Learning for Generalization in Imitation and Reinforcement Learning
imitation representations dynamics generalization policies invariant causal representation reinforcement fundamental challenge reinforcement learning
A fundamental challenge in imitation and reinforcement learning is to learn policies, representations, or dynamics that do not build on spurious correlations and generalize beyond the specific environments that they were trained on. We investigate these generalization problems from a unified view. For this, we propose a general framework to tackle them with theoretical guarantees on both identifiability and generalizability under mild assumptions on environmental changes. By leveraging a diverse set of training environments, we construct a data representation that ignores any spurious features and consistently predicts target variables well across environments. Following this approach, we build invariant predictors in terms of policy, representations, and dynamics. We theoretically show that the resulting policies, representations, and dynamics are able to generalize to unseen environments. Extensive experiments on both synthetic and real-world datasets show that our methods attain improved generalization over a variety of baselines.
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A framework for learned CountSketch
Compression sketching
Sketching is a compression technique that can be applied to many problems to solve them quickly and approximately. The matrices used to project data to smaller dimensions are called "sketches". In this work, we consider the problem of optimizing sketches to obtain low approximation error over a data distribution.
We introduce a general framework for "learning" and applying CountSketch, a type of sparse sketch. The sketch optimization procedure has two stages: one for optimizing the placements of the sketch's non-zero entries and another for optimizing their values. Next, we provide a way to apply learned sketches that has worst-case guarantees for approximation error.
We instantiate this framework with three sketching applications: least-squares regression, low-rank approximation (LRA), and k-means clustering. Our experiments demonstrate that our approach substantially decreases approximation error compared to classical and naively learned sketches.
Finally, we investigate the theoretical aspects of our approach. For regression and LRA, we show that our method obtains state-of-the art accuracy for fixed time complexity. For LRA, we prove that it is strictly better to include the first optimization stage for two standard input distributions. For k-means, we derive a more straightforward means of retaining approximation guarantees.
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Improved Autoregressive Modeling with Distribution Smoothing
generative models autoregressive models
While autoregressive models excel at image compression, their sample quality is often lacking. Although not realistic, generated images often have high likelihood according to the model, resembling the case of adversarial examples. Inspired by a successful adversarial defense method, we incorporate randomized smoothing into autoregressive generative modeling. We first model a smoothed version of the data distribution, and then reverse the smoothing process to recover the original data distribution. This procedure drastically improves the sample quality of existing autoregressive models on several synthetic and real-world image datasets while obtaining competitive likelihoods on synthetic datasets.
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Composite Adversarial Training for Multiple Adversarial Perturbations and Beyond
adversarial examples deep learning robustness
One intriguing property of deep neural networks (DNNs) is their vulnerability to adversarial perturbations. Despite the plethora of work on defending against individual perturbation models, improving DNN robustness against the combinations of multiple perturbations is still fairly under-studied. In this paper, we propose \underline{c}omposite \underline{a}dversarial \underline{t}raining (CAT), a novel training method that flexibly integrates and optimizes multiple adversarial losses, leading to significant robustness improvement with respect to individual perturbations as well as their ``compositions''. Through empirical evaluation on benchmark datasets and models, we show that CAT outperforms existing adversarial training methods by large margins in defending against the compositions of pixel perturbations and spatial transformations, two major classes of adversarial perturbation models, while incurring limited impact on clean inputs.
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Dynamic Graph Representation Learning with Fourier Temporal State Embedding
Graph Neural Networks Dynamic Graph Signal Processing
Static graph representation learning has been applied in many tasks over the years thanks to the invention of unsupervised graph embedding methods and more recently, graph neural networks (GNNs). However, in many cases, we are to handle dynamic graphs where the structures of graphs and labels of the nodes are evolving steadily with time. This has posed a great challenge to existing methods in time and memory efficiency. In this work, we present a new method named Fourier Temporal State Embedding (FTSE) to address the temporal information in dynamic graph representation learning. FTSE offered time and memory-efficient solution through applying signal processing techniques to the temporal graph signals. We paired the Fourier Transform with an efficient edge network and provided a new prototype of modeling dynamic graph evolution with high precision. FTSE can also prevent the 'history explosion' that exists in sequential models. The empirical study shows that our proposed approach achieves significantly better performance than previous approaches on public datasets across multiple tasks.
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Early Stopping by Gradient Disparity
Supervised Representation Learning Deep Neural Networks Generalization Early Stopping
Validation-based early-stopping methods are one of the most popular techniques used to avoid over-training deep neural networks. They require to set aside a reliable unbiased validation set, which can be expensive in applications offering limited amounts of data. In this paper, we propose to use \emph{gradient disparity}, which we define as the $\ell_2$ norm distance between the gradient vectors of two batches drawn from the training set. It comes from a probabilistic upper bound on the difference between the classification errors over a given batch, when the network is trained on this batch and when the network is trained on another batch of points sampled from the same dataset. We empirically show that gradient disparity is a very promising early-stopping criterion when data is limited, because it uses all the training samples during training. Furthermore, we show in a wide range of experimental settings that gradient disparity is not only strongly related to the usual generalization error between the training and test sets, but that it is also much more informative about the level of label noise.
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Multi-Representation Ensemble in Few-Shot Learning
Ensemble learning Few shot learning Multi-representaion
Deep neural networks (DNNs) compute representations in a layer by layer fashion, producing a final representation at the top layer of the pipeline, and classification or regression is made using the final representation. A number of DNNs (e.g., ResNet, DenseNet) have shown that representations from the earlier layers can be beneficial. They improved performance by aggregating representations from different layers. In this work, we asked the question, besides forming an aggregation, whether these representations can be utilized directly with the classification layer(s) to obtain better performance. We started our quest to the answer by investigating the classifiers based on the representations from different layers and observed that these classifiers were diverse and many of their decisions were complementary to each other, hence having the potential to generate a better overall decision when combined. Following this observation, we propose an ensemble method that creates an ensemble of classifiers, each taking a representation from a different depth of a base DNN as the input. We tested this ensemble method in the setting of few-shot learning. Experiments were conducted on the mini-ImageNet and tieredImageNet datasets which are commonly used in the evaluation of few-shot learning methods. Our ensemble achieves the new state-of-the-art results for both datasets, comparing to previous regular and ensemble approaches.
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What are effective labels for augmented data? Improving robustness with AutoLabel
data augmentation image classification calibration distributional shifts adversarial robustness
A wide breadth of research has devised data augmentation approaches that can improve both accuracy and generalization performance for neural networks. However, augmented data can end up being far from the clean data and what is the appropriate label is less clear. Despite this, most existing work simply reuses the original label from the clean data, and the choice of label accompanying the augmented data is relatively less explored. In this paper, we propose AutoLabel to automatically learn the labels for augmented data, based on the distance between the clean distribution and augmented distribution. AutoLabel is built on label smoothing and is guided by the calibration-performance over a hold-out validation set. We show that AutoLabel is a generic framework that can be easily applied to existing data augmentation methods, including AugMix, mixup, and adversarial training. Experiments on CIFAR-10, CIFAR-100 and ImageNet show that AutoLabel can improve models' accuracy and calibration performance, especially under distributional shift. Additionally, we demonstrate that AutoLabel can help adversarial training by bridging the gap between clean accuracy and adversarial robustness.
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Federated Learning of a Mixture of Global and Local Models
optimization federated learning personalization local SGD
We propose a new optimization formulation for training federated learning models. The standard formulation has the form of an empirical risk minimization problem constructed to find a single global model trained from the private data stored across all participating devices. In contrast, our formulation seeks an explicit trade-off between this traditional global model and the local models, which can be learned by each device from its own private data without any communication. Further, we develop several efficient variants of SGD (with and without partial participation and with and without variance reduction) for solving the new formulation and prove communication complexity guarantees. Notably, our methods are similar but not identical to federated averaging / local SGD, thus shedding some light on the essence of the elusive method. In particular, our methods do not perform full averaging steps and instead merely take steps towards averaging. We argue for the benefits of this new paradigm for federated learning.
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Testing Robustness Against Unforeseen Adversaries
adversarial examples adversarial training adversarial attacks
Most existing adversarial defenses only measure robustness to $L_p$ adversarial attacks. Not only are adversaries unlikely to exclusively create small $L_p$ perturbations, adversaries are unlikely to remain fixed. Adversaries adapt and evolve their attacks; hence adversarial defenses must be robust to a broad range of unforeseen attacks. We address this discrepancy between research and reality by proposing a new evaluation framework called ImageNet-UA. Our framework enables the research community to test ImageNet model robustness against attacks not encountered during training. To create ImageNet-UA's diverse attack suite, we introduce a total of four novel adversarial attacks. We also demonstrate that, in comparison to ImageNet-UA, prevailing $L_\infty$ robustness assessments give a narrow account of adversarial robustness. By evaluating current defenses with ImageNet-UA, we find they provide little robustness to unforeseen attacks. We hope the greater variety and realism of ImageNet-UA enables development of more robust defenses which can generalize beyond attacks seen during training.
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Sliced Kernelized Stein Discrepancy
kernel methods variational inference particle inference
Kernelized Stein discrepancy (KSD), though being extensively used in goodness-of-fit tests and model learning, suffers from the curse-of-dimensionality. We address this issue by proposing the sliced Stein discrepancy and its scalable and kernelized variants, which employs kernel-based test functions defined on the optimal one-dimensional projections. When applied to goodness-of-fit tests, extensive experiments show the proposed discrepancy significantly outperforms KSD and various baselines in high dimensions. For model learning, we show its advantages by training an independent component analysis when compared with existing Stein discrepancy baselines. We further propose a novel particle inference method called sliced Stein variational gradient descent (S-SVGD) which alleviates the mode-collapse issue of SVGD in training variational autoencoders.
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Learning To Ground Decentralized Multi-Agent Communication with Contrastive Learning
Multi-Agent Reinforcement Learning Reinforcement Learning Deep Reinforcement Learning Deep Multi-Agent Reinforcement Learning Deep Learning Multi-Agent Systems
For communication to happen successfully, a common language is required between agents to understand information communicated by one another. Inducing the emergence of a common language has been a difficult challenge to multi-agent learning systems. In this work, we introduce an alternative perspective to the communicative messages sent between agents, considering them as different incomplete views of the environment state. Based on this perspective, we propose a simple approach to induce the emergence of a common language by maximizing the mutual information between messages of a given trajectory in a self-supervised manner. By evaluating our method in communication-essential environments, we empirically show how our method leads to better learning performance and speed, and learns a more consistent common language than existing methods, without introducing additional learning parameters.
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Learning Long-term Visual Dynamics with Region Proposal Interaction Networks
dynamics prediction interaction networks physical reasoning
Learning long-term dynamics models is the key to understanding physical common sense. Most existing approaches on learning dynamics from visual input sidestep long-term predictions by resorting to rapid re-planning with short-term models. This not only requires such models to be super accurate but also limits them only to tasks where an agent can continuously obtain feedback and take action at each step until completion. In this paper, we aim to leverage the ideas from success stories in visual recognition tasks to build object representations that can capture inter-object and object-environment interactions over a long range. To this end, we propose Region Proposal Interaction Networks (RPIN), which reason about each object's trajectory in a latent region-proposal feature space. Thanks to the simple yet effective object representation, our approach outperforms prior methods by a significant margin both in terms of prediction quality and their ability to plan for downstream tasks, and also generalize well to novel environments. Code, pre-trained models, and more visualization results are available at https://haozhi.io/RPIN.
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Identifying Treatment Effects under Unobserved Confounding by Causal Representation Learning
VAE variational autoencoder Representation Learning treatment effects causal inference Unobserved Confounding identifiability CATE ATE
As an important problem of causal inference, we discuss the estimation of treatment effects under the existence of unobserved confounding. By representing the confounder as a latent variable, we propose Counterfactual VAE, a new variant of variational autoencoder, based on recent advances in identifiability of representation learning. Combining the identifiability and classical identification results of causal inference, under mild assumptions on the generative model and with small noise on the outcome, we theoretically show that the confounder is identifiable up to an affine transformation and then the treatment effects can be identified. Experiments on synthetic and semi-synthetic datasets demonstrate that our method matches the state-of-the-art, even under settings violating our formal assumptions.
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A Generative Approach to LLM Harmfulness Detection with Red Flag Tokens
Large Language Models Adversarial Robustness
Most safety training methods for large-language models (LLMs) based on fine-tuning rely on dramatically changing the output distribution of the model when faced with a harmful request, shifting it from an unsafe answer to a refusal to respond.
These methods inherently compromise model capabilities and might make auto-regressive models vulnerable to attacks that make likely an initial token of affirmative response.
To avoid that, we propose to expand the model's vocabulary with a special token we call a *red flag token* ($\langle RF \rangle$) and propose to fine-tune the model to generate this token at any time harmful content is generated or about to be generated.
This novel safety training method effectively augments LLMs into generative classifiers of harmfulness at all times during the conversation.
This method offers several advantages: it enables the model to explicitly learn the concept of harmfulness while marginally affecting the generated distribution, thus maintaining the model's utility.
It also evaluates each generated answer rather than just the input prompt and provides a stronger defence against sampling-based attacks.
In addition, it simplifies the evaluation of the model's robustness and reduces correlated failures when combined with a classifier.
We further show robustness to long contexts, and supervised fine-tuning attacks.
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not-so-big-GAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution
deep generative modeling GAN super-resolution wavelet transformation energy efficient
State-of-the-art models for high-resolution image generation, such as BigGAN and VQVAE-2, require an incredible amount of compute resources and/or time (512 TPU-v3 cores) to train, putting them out of reach for the larger research community. On the other hand, GAN-based image super-resolution models, such as ESRGAN, can not only upscale images to high dimensions, but also are efficient to train. In this paper, we present not-so-big-GAN (nsb-GAN), a simple yet cost-effective two-step training framework for deep generative models (DGMs) of high-dimensional natural images. First, we generate images in low-frequency bands by training a sampler in the wavelet domain. Then, we super-resolve these images from the wavelet domain back to the pixel-space with our novel wavelet super-resolution decoder network. Wavelet-based down-sampling method preserves more structural information than pixel-based methods, leading to significantly better generative quality of the low-resolution sampler (e.g., 64×64). Since the sampler and decoder can be trained in parallel and operate on much lower dimensional spaces than end-to-end models, the training cost is substantially reduced. On ImageNet 512×512, our model achieves a Fréchet Inception Distance (FID) of 10.59 – beating the baseline BigGAN model – at half the compute (256 TPU-v3 cores).
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Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity
Data Augmentation Deep Learning Supervised Learning Discrete Optimization
While deep neural networks show great performance on fitting to the training distribution, improving the networks' generalization performance to the test distribution and robustness to the sensitivity to input perturbations still remain as a challenge. Although a number of mixup based augmentation strategies have been proposed to partially address them, it remains unclear as to how to best utilize the supervisory signal within each input data for mixup from the optimization perspective. We propose a new perspective on batch mixup and formulate the optimal construction of a batch of mixup data maximizing the data saliency measure of each individual mixup data and encouraging the supermodular diversity among the constructed mixup data. This leads to a novel discrete optimization problem minimizing the difference between submodular functions. We also propose an efficient modular approximation based iterative submodular minimization algorithm for efficient mixup computation per each minibatch suitable for minibatch based neural network training. Our experiments show the proposed method achieves the state of the art generalization, calibration, and weakly supervised localization results compared to other mixup methods. The source code is available at https://github.com/snu-mllab/Co-Mixup.
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Faster Binary Embeddings for Preserving Euclidean Distances
Binary Embeddings Johnson-Lindenstrauss Transforms Sigma Delta Quantization
We propose a fast, distance-preserving, binary embedding algorithm to transform a high-dimensional dataset $\mathcal{T}\subseteq\mathbb{R}^n$ into binary sequences in the cube $\{\pm 1\}^m$. When $\mathcal{T}$ consists of well-spread (i.e., non-sparse) vectors, our embedding method applies a stable noise-shaping quantization scheme to $A x$ where $A\in\mathbb{R}^{m\times n}$ is a sparse Gaussian random matrix. This contrasts with most binary embedding methods, which usually use $x\mapsto \mathrm{sign}(Ax)$ for the embedding. Moreover, we show that Euclidean distances among the elements of $\mathcal{T}$ are approximated by the $\ell_1$ norm on the images of $\{\pm 1\}^m$ under a fast linear transformation. This again contrasts with standard methods, where the Hamming distance is used instead. Our method is both fast and memory efficient, with time complexity $O(m)$ and space complexity $O(m)$ on well-spread data. When the data is not well-spread, we show that the approach still works provided that data is transformed via a Walsh-Hadamard matrix, but now the cost is $O(n\log n)$ per data point. Further, we prove that the method is accurate and its associated error is comparable to that of a continuous valued Johnson-Lindenstrauss embedding plus a quantization error that admits a polynomial decay as the embedding dimension $m$ increases.
Thus the length of the binary codes required to achieve a desired accuracy is quite small, and we show it can even be compressed further without compromising the accuracy. To illustrate our results, we test the proposed method on natural images and show that it achieves strong performance.
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Cortico-cerebellar networks as decoupled neural interfaces
systems neuroscience cerebellum neocortex decoupled neural interfaces deep learning decorrelation inverse models forward models
The brain solves the credit assignment problem remarkably well. For credit to be correctly assigned across multiple cortical areas a given area should, in principle, wait for others to finish their computation. How the brain deals with this locking problem has remained unclear. Deep learning methods suffer from similar locking constraints both on the forward and backward phase. Recently, decoupled neural interfaces (DNI) were introduced as a solution to the forward and backward locking problems.
Here we propose that a specialised brain region, the cerebellum, helps the cerebral cortex solve the locking problem closely matching the computations and architecture of DNI. In particular, we propose that classical cerebellar forward and inverse models are equivalent to solving the backward and forward locking problems, respectively. To demonstrate the potential of this framework we focus on modelling a given brain area as a recurrent neural network in which the cerebellum approximates temporal feedback signals as provided by BPTT. We tested the cortico-cerebellar-DNI (CC-DNI) model in a range of sensorimotor and cognitive tasks that have been shown to be cerebellar-dependent. First, we show that the CC-DNI unlocking mechanisms can facilitate learning in a simple target reaching task. Next, by building on the sequential MNIST task we demonstrate that these results generalise to more complex sensorimotor tasks. Our cortico-cerebellar model readily applies to a wider range of modalities, to demonstrate this we tested the model in a cognitive task, caption generation. Models without the cerebellar-DNI component exhibit deficits similar to those observed in cerebellar patients in both motor and cognitive tasks. Moreover, we used CC-DNI to generate a set of specific neuroscience predictions. Finally, we introduce a CC-DNI model with highly sparse connectivity as observed in the cerebellum, which substantially reduces the number of parameters while improving learning through decorrelation.
Overall, our work offers a novel perspective on the cerebellum as a brain-wide decoupling machine for efficient credit assignment and opens a new avenue of research between deep learning and neuroscience.
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Encoded Prior Sliced Wasserstein AutoEncoder for learning latent manifold representations
VAE sliced Wasserstein distance latent representation interpolation manifold embedding geodesics network algorithm
While variational autoencoders have been successful in a variety of tasks, the use of conventional Gaussian or Gaussian mixture priors are limited in their ability to encode underlying structure of data in the latent representation.
In this work, we introduce an Encoded Prior Sliced Wasserstein AutoEncoder (EPSWAE) wherein an additional prior-encoder network facilitates learns an embedding of the data manifold which preserves topological and geometric properties of the data, thus improving the structure of latent space.
The autoencoder and prior-encoder networks are iteratively trained using the Sliced Wasserstein (SW) distance, which efficiently measures the distance between two \textit{arbitrary} sampleable distributions without being constrained to a specific form as in the KL divergence, and without requiring expensive adversarial training.
To improve the representation, we use (1) a structural consistency term in the loss that encourages isometry between feature space and latent space and (2) a nonlinear variant of the SW distance which averages over random nonlinear shearing.
The effectiveness of the learned manifold encoding is best explored by traversing the latent space through interpolations along \textit{geodesics} which generate samples that lie on the manifold and hence are advantageous compared to standard Euclidean interpolation.
To this end, we introduce a graph-based algorithm for interpolating along network-geodesics in latent space by maximizing the density of samples along the path while minimizing total energy. We use the 3D-spiral data to show that the prior does indeed encode the geometry underlying the data and to demonstrate the advantages of the network-algorithm for interpolation.
Additionally, we apply our framework to MNIST, and CelebA datasets, and show that outlier generations, latent representations, and geodesic interpolations are comparable to the state of the art.
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Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective
Neural Architecture Search neural tangent kernel number of linear regions
Neural Architecture Search (NAS) has been explosively studied to automate the discovery of top-performer neural networks. Current works require heavy training of supernet or intensive architecture evaluations, thus suffering from heavy resource consumption and often incurring search bias due to truncated training or approximations. Can we select the best neural architectures without involving any training and eliminate a drastic portion of the search cost?
We provide an affirmative answer, by proposing a novel framework called \textit{training-free neural architecture search} ($\textbf{TE-NAS}$). TE-NAS ranks architectures by analyzing the spectrum of the neural tangent kernel (NTK), and the number of linear regions in the input space. Both are motivated by recent theory advances in deep networks, and can be computed without any training. We show that: (1) these two measurements imply the $\textit{trainability}$ and $\textit{expressivity}$ of a neural network; and (2) they strongly correlate with the network's actual test accuracy. Further on, we design a pruning-based NAS mechanism to achieve a more flexible and superior trade-off between the trainability and expressivity during the search. In NAS-Bench-201 and DARTS search spaces, TE-NAS completes high-quality search but only costs $\textbf{0.5}$ and $\textbf{4}$ GPU hours with one 1080Ti on CIFAR-10 and ImageNet, respectively. We hope our work to inspire more attempts in bridging between the theoretic findings of deep networks and practical impacts in real NAS applications.
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Least Probable Disagreement Region for Active Learning
active learning uncertainty sampling disagreement region variation ratio deep learning distance decision boundary
Active learning strategy to query unlabeled samples nearer the estimated decision boundary at each step has been known to be effective when the distance from the sample data to the decision boundary can be explicitly evaluated; however, in numerous cases in machine learning, especially when it involves deep learning, conventional distance such as the $\ell_p$ from sample to decision boundary is not readily measurable. This paper defines a theoretical distance of unlabeled sample to the decision boundary as the least probable disagreement region (LPDR) containing the unlabeled sample, and it discusses how this theoretical distance can be empirically evaluated with a lower order of time complexity. Monte Carlo sampling of the hypothesis is performed in approximating the theoretically defined distance. Experimental results on various datasets show that the proposed algorithm consistently outperforms all other high performing uncertainty based active learning algorithms and leads to state-of-the-art active learning performance on CIFAR10, CIFAR100, Tiny ImageNet and Food101 datasets. Only the proposed algorithm outperforms random sampling on CIFAR100 dataset using K-CNN while all other algorithms fail to do so.
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Leveraging affinity cycle consistency to isolate factors of variation in learned representations
variation factors acc affinity cycle consistency learned representations data dataset generative approaches groupings supervision
Identifying the dominant factors of variation across a dataset is a central goal of representation learning. Generative approaches lead to descriptions that are rich enough to recreate the data, but often only a partial description is needed to complete downstream tasks or to gain insights about the dataset. In this work, we operate in the setting where limited information is known about the data in the form of groupings, or set membership, and the task is to learn representations which isolate the factors of variation that are common across the groupings. Our key insight is the use of affinity cycle consistency (ACC) between the learned embeddings of images belonging to different sets. In contrast to prior work, we demonstrate that ACC can be applied with significantly fewer constraints on the factors of variation, across a remarkably broad range of settings, and without any supervision for half of the data. By curating datasets from Shapes3D, we quantify the effectiveness of ACC through mutual information between the learned representations and the known generative factors. In addition, we demonstrate the applicability of ACC to the tasks of digit style isolation and synthetic-to-real object pose transfer and compare to generative approaches utilizing the same supervision.
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Conditional Generative Modeling via Learning the Latent Space
Multimodal Spaces Conditional Generation Generative Modeling
Although deep learning has achieved appealing results on several machine learning tasks, most of the models are deterministic at inference, limiting their application to single-modal settings. We propose a novel general-purpose framework for conditional generation in multimodal spaces, that uses latent variables to model generalizable learning patterns while minimizing a family of regression cost functions. At inference, the latent variables are optimized to find solutions corresponding to multiple output modes. Compared to existing generative solutions, our approach demonstrates faster and more stable convergence, and can learn better representations for downstream tasks. Importantly, it provides a simple generic model that can perform better than highly engineered pipelines tailored using domain expertise on a variety of tasks, while generating diverse outputs. Code available at https://github.com/samgregoost/cGML.
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Physics-aware, probabilistic model order reduction with guaranteed stability
inductive bias probabilistic generative models state-space models model order reduction slowness long-term stability
Given (small amounts of) time-series' data from a high-dimensional, fine-grained, multiscale dynamical system, we propose a generative framework for learning an effective, lower-dimensional, coarse-grained dynamical model that is predictive of the fine-grained system's long-term evolution but also of its behavior under different initial conditions.
We target fine-grained models as they arise in physical applications (e.g. molecular dynamics, agent-based models), the dynamics of which are strongly non-stationary but their transition to equilibrium is governed by unknown slow processes which are largely inaccessible by brute-force simulations.
Approaches based on domain knowledge heavily rely on physical insight in identifying temporally slow features and fail to enforce the long-term stability of the learned dynamics. On the other hand, purely statistical frameworks lack interpretability and rely on large amounts of expensive simulation data (long and multiple trajectories) as they cannot infuse domain knowledge.
The generative framework proposed achieves the aforementioned desiderata by employing a flexible prior on the complex plane for the latent, slow processes, and an intermediate layer of physics-motivated latent variables that reduces reliance on data and imbues inductive bias. In contrast to existing schemes, it does not require the a priori definition of projection operators from the fine-grained description and addresses simultaneously the tasks of dimensionality reduction and model estimation.
We demonstrate its efficacy and accuracy in multiscale physical systems of particle dynamics where probabilistic, long-term predictions of phenomena not contained in the training data are produced.
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Discovering Non-monotonic Autoregressive Orderings with Variational Inference
variational inference unsupervised learning computer vision natural language processing optimization reinforcement learning
The predominant approach for language modeling is to encode a sequence of tokens from left to right, but this eliminates a source of information: the order by which the sequence was naturally generated. One strategy to recover this information is to decode both the content and ordering of tokens. Some prior work supervises content and ordering with hand-designed loss functions to encourage specific orders or bootstraps from a predefined ordering. These approaches require domain-specific insight. Other prior work searches over valid insertion operations that lead to ground truth sequences during training, which has high time complexity and cannot be efficiently parallelized. We address these limitations with an unsupervised learner that can be trained in a fully-parallelizable manner to discover high-quality autoregressive orders in a data driven way without a domain-specific prior. The learner is a neural network that performs variational inference with the autoregressive ordering as a latent variable. Since the corresponding variational lower bound is not differentiable, we develop a practical algorithm for end-to-end optimization using policy gradients. Strong empirical results with our solution on sequence modeling tasks suggest that our algorithm is capable of discovering various autoregressive orders for different sequences that are competitive with or even better than fixed orders.
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PPLM Revisited: Steering and Beaming a Lumbering Mammoth to Control Text Generation
natural language generation language models pplm reproducibility generalization
Dathathri et al., 2020 introduced the Plug and Play Language Model (PPLM) as a configurable method for controlled language generation. Since then, the publication has been cited more than 200 times and the official implementation received >800 stars on GitHub. Such popularity can be explained by the fact that PPLM achieves fine-grained control of attributes such as topics and sentiment while retaining fluency without retraining the language model (LM).
In a blogpost accompanying the original paper, the authors compared any LM with an “unguided mammoth” whilst PPLM was presented as a mouse sitting on top of the mammoth who steers it in the right direction. However, the interplay between the prompt and the BoW is underexplored in the paper and the original blog post. Moreover, to the best of our knowledge, the impact of the interplay between the prompt and the BoW on the controllability of the text has also not yet been described elsewhere.
In this blog post, we address both of the issues as well as the issue of the reproducibility and sensitivity to hyperparameter settings of PPLM. We aim to provide more insights into the workings of PPLM and, to some extent, into Natural Language Generation (NLG) controllability in general.
Based on the results of the experiments we conclude that producing topic related texts is difficult with the hyperparameters provided in the original paper, but pushing the distribution of the generated words more towards the targeted subject leads to enhanced results. On top of that, we also conclude that carefully picking the words in the BoW is important to control the PPLM, and modifying the weight might help control the PPLM to generate the texts that are relevant to the exact words that we like.
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HybridNet: A Hybrid Neural Architecture to Speed-up Autoregressive Models
neural architecture inference time reduction hybrid model
This paper introduces HybridNet, a hybrid neural network to speed-up autoregressive
models for raw audio waveform generation. As an example, we propose
a hybrid model that combines an autoregressive network named WaveNet and a
conventional LSTM model to address speech synthesis. Instead of generating
one sample per time-step, the proposed HybridNet generates multiple samples per
time-step by exploiting the long-term memory utilization property of LSTMs. In
the evaluation, when applied to text-to-speech, HybridNet yields state-of-art performance.
HybridNet achieves a 3.83 subjective 5-scale mean opinion score on
US English, largely outperforming the same size WaveNet in terms of naturalness
and provide 2x speed up at inference.
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Grey-box Extraction of Natural Language Models
language models transformer model extraction security
Model extraction attacks attempt to replicate a target machine learning model from predictions obtained by querying its inference API. Most existing attacks on Deep Neural Networks achieve this by supervised training of the copy using the victim's predictions. An emerging class of attacks exploit algebraic properties of DNNs to obtain high-fidelity copies using orders of magnitude fewer queries than the prior state-of-the-art. So far, such powerful attacks have been limited to networks with few hidden layers and ReLU activations.
In this paper we present algebraic attacks on large-scale natural language models in a grey-box setting, targeting models with a pre-trained (public) encoder followed by a single (private) classification layer. Our key observation is that a small set of arbitrary embedding vectors is likely to form a basis of the classification layer's input space, which a grey-box adversary can compute. We show how to use this information to solve an equation system that determines the classification layer from the corresponding probability outputs.
We evaluate the effectiveness of our attacks on different sizes of transformer models and downstream tasks. Our key findings are that (i) with frozen base layers, high-fidelity extraction is possible with a number of queries that is as small as twice the input dimension of the last layer. This is true even for queries that are entirely in-distribution, making extraction attacks indistinguishable from legitimate use; (ii) with fine-tuned base layers, the effectiveness of algebraic attacks decreases with the learning rate, showing that fine-tuning is not only beneficial for accuracy but also indispensable for model confidentiality.
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A generalized probability kernel on discrete distributions and its application in two-sample test
maximum mean discrepancy RKHS two-sample test empirical estimator discrete distributions
We propose a generalized probability kernel(GPK) on discrete distributions with finite support. This probability kernel, defined as kernel between distributions instead of samples, generalizes the existing discrepancy statistics such as maximum mean discrepancy(MMD) as well as probability product kernels, and extends to more general cases. For both existing and newly proposed statistics, we estimate them through empirical frequency and illustrate the strategy to analyze the resulting bias and convergence bounds. We further propose power-MMD, a natural extension of MMD in the framework of GPK, illustrating its usage for the task of two-sample test. Our work connects the fields of discrete distribution-property estimation and kernel-based hypothesis test, which might shed light on more new possibilities.
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Evaluation of Large Language Models via Coupled Token Generation
LLM Evaluation Machine Learning Causality
State of the art large language models rely on randomization to respond to a prompt. As an immediate consequence, a model may respond differently to the same prompt if asked multiple times. In this work, we argue that the evaluation and ranking of large language models should control for the randomization underpinning their functioning. Our starting point is the development of a causal model for coupled autoregressive generation, which allows different large language models to sample responses with the same source of randomness. Building upon our causal model, we first show that, on evaluations based on benchmark datasets, coupled autoregressive generation leads to the same conclusions as vanilla autoregressive generation but using provably fewer samples. However, we further show that, on evaluations based on pairwise comparisons, coupled and vanilla autoregressive generation can surprisingly lead to different rankings when comparing more than two models, even with an infinite amount of samples. This suggests that the apparent advantage of a model over others in existing evaluation protocols may not be genuine but rather confounded by the randomness inherent to the generation process. To illustrate and complement our theoretical results, we conduct experiments with several large language models from the Llama family. We find that, across multiple knowledge areas from the popular MMLU benchmark dataset, coupled autoregressive generation requires up to 40% fewer samples to reach the same conclusions as vanilla autoregressive generation. Further, using data from the LMSYS Chatbot Arena platform, we find that the win-rates derived from pairwise comparisons by a strong large language model to prompts differ under coupled and vanilla autoregressive generation.
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Zero-shot Fairness with Invisible Demographics
fairness missing data adversary classification disentanglement
In a statistical notion of algorithmic fairness, we partition individuals into groups based on some key demographic factors such as race and gender, and require that some statistics of a classifier be approximately equalized across those groups. Current approaches require complete annotations for demographic factors, or focus on an abstract worst-off group rather than demographic groups. In this paper, we consider the setting where the demographic factors are only partially available. For example, we have training examples for white-skinned and dark-skinned males, and white-skinned females, but we have zero examples for dark-skinned females. We could also have zero examples for females regardless of their skin colors. Without additional knowledge, it is impossible to directly control the discrepancy of the classifier's statistics for those invisible groups. We develop a disentanglement algorithm that splits a representation of data into a component that captures the demographic factors and another component that is invariant to them based on a context dataset. The context dataset is much like the deployment dataset, it is unlabeled but it contains individuals from all demographics including the invisible. We cluster the context set, equalize the cluster size to form a "perfect batch", and use it as a supervision signal for the disentanglement. We propose a new discriminator loss based on a learnable attention mechanism to distinguish a perfect batch from a non-perfect one. We evaluate our approach on standard classification benchmarks and show that it is indeed possible to protect invisible demographics.
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A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning Algorithms
statistical testing reinforcement learning reproducibility replicability random seeds
Consistently checking the statistical significance of experimental results is the first mandatory step towards reproducible science. This paper presents a hitchhiker's guide to rigorous comparisons of reinforcement learning algorithms. After introducing the concepts of statistical testing, we review the relevant statistical tests and compare them empirically in terms of false positive rate and statistical power as a function of the sample size (number of seeds) and effect size. We further investigate the robustness of these tests to violations of the most common hypotheses (normal distributions, same distributions, equal variances). Beside simulations, we compare empirical distributions obtained by running Soft-Actor Critic and Twin-Delayed Deep Deterministic Policy Gradient on Half-Cheetah. We conclude by providing guidelines and code to perform rigorous comparisons of RL algorithm performances.
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Autonomous Vehicle Fleet Coordination With Deep Reinforcement Learning
Deep Reinforcement Learning mult-agent systems
Autonomous vehicles are becoming more common in city transportation. Companies will begin to find a need to teach these vehicles smart city fleet coordination. Currently, simulation based modeling along with hand coded rules dictate the decision making of these autonomous vehicles. We believe that complex intelligent behavior can be learned by these agents through Reinforcement Learning.In this paper, we discuss our work for solving this system by adapting the Deep Q-Learning (DQN) model to the multi-agent setting. Our approach applies deep reinforcement learning by combining convolutional neural networks with DQN to teach agents to fulfill customer demand in an environment that is partially observ-able to them. We also demonstrate how to utilize transfer learning to teach agents to balance multiple objectives such as navigating to a charging station when its en-ergy level is low. The two evaluations presented show that our solution has shown hat we are successfully able to teach agents cooperation policies while balancing multiple objectives.
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PATTERNS AND MECHANISMS OF CONTRASTIVE ACTIVATION ENGINEERING
LLMs activation steering representation engineering controlled text generation safety alignment
Controlling the behavior of Large Language Models (LLMs) remains a significant
challenge due to their inherent complexity and opacity. While techniques
like fine-tuning can modify model behavior, they typically require extensive
computational resources. Recent work has introduced a class of contrastive
activation engineering (CAE) techniques as promising approaches for steering
LLM outputs through targeted modifications to their internal representations.
Applied at inference-time with zero cost, CAE has the potential to introduce
a new paradigm of flexible, task-specific LLM behavior tuning. We analyze
the performance of CAE in in-distribution, out-of-distribution settings, evaluate
drawbacks, and begin to develop comprehensive guidelines for its effective
deployment. We find that 1. CAE is only reliably effective when applied to
in-distribution contexts. 2. Increasing the number of samples used to generate
steering vectors has diminishing returns at around 80 samples. 3. Steering vectors
are susceptible to adversarial inputs that reverses the behavior that is steered for.
4. Steering vectors harm the overall model perplexity. 5. Larger models are more
resistant to steering-induced degradation.
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A Learned Representation for Scalable Vector Graphics
Representation Learning Image Synthesis Computer Vision Applications Deep Learning Scalable Vector Graphics Font Generation
Dramatic advances in generative models have resulted in near photographic quality for artificially rendered faces, animals and other objects in the natural world.In spite of such advances, a higher level understanding of vision and imagery does not arise from exhaustively modeling an object, but instead identifying higher-level attributes that best summarize the aspects of an object. In this work we attempt to model the drawing process of fonts by building sequential generative models of vector graphics. This model has the benefit of providing a scale-invariant representation for imagery whose latent representation may be systematically manipulated and exploited to perform style propagation. We demonstrate these results on a large dataset of fonts and highlight how such a model captures the statistical dependencies and richness of this dataset. We envision that our model can find use as a tool for designers to facilitate font design.
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A Representational Model of Grid Cells' Path Integration Based on Matrix Lie Algebras
grid cells path integration representational model Lie algebras error correction
The grid cells in the mammalian medial entorhinal cortex exhibit striking hexagon firing patterns when the agent navigates in the open field. It is hypothesized that the grid cells are involved in path integration so that the agent is aware of its self-position by accumulating its self-motion. Assuming the grid cells form a vector representation of self-position, we elucidate a minimally simple recurrent model for grid cells' path integration based on two coupled matrix Lie algebras that underlie two coupled rotation systems that mirror the agent's self-motion: (1) When the agent moves along a certain direction, the vector is rotated by a generator matrix. (2) When the agent changes direction, the generator matrix is rotated by another generator matrix. Our experiments show that our model learns hexagonal grid response patterns that resemble the firing patterns observed from the grid cells in the brain. Furthermore, the learned model is capable of near exact path integration, and it is also capable of error correction. Our model is novel and simple, with explicit geometric and algebraic structures.
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Smooth Loss Functions for Deep Top-k Classification
classification loss smooth loss functions deep performance deep neural networks loss function family algorithm
The top-$k$ error is a common measure of performance in machine learning and computer vision. In practice, top-$k$ classification is typically performed with deep neural networks trained with the cross-entropy loss. Theoretical results indeed suggest that cross-entropy is an optimal learning objective for such a task in the limit of infinite data. In the context of limited and noisy data however, the use of a loss function that is specifically designed for top-$k$ classification can bring significant improvements.
Our empirical evidence suggests that the loss function must be smooth and have non-sparse gradients in order to work well with deep neural networks. Consequently, we introduce a family of smoothed loss functions that are suited to top-$k$ optimization via deep learning. The widely used cross-entropy is a special case of our family. Evaluating our smooth loss functions is computationally challenging: a na{\"i}ve algorithm would require $\mathcal{O}(\binom{n}{k})$ operations, where $n$ is the number of classes. Thanks to a connection to polynomial algebra and a divide-and-conquer approach, we provide an algorithm with a time complexity of $\mathcal{O}(k n)$. Furthermore, we present a novel approximation to obtain fast and stable algorithms on GPUs with single floating point precision. We compare the performance of the cross-entropy loss and our margin-based losses in various regimes of noise and data size, for the predominant use case of $k=5$. Our investigation reveals that our loss is more robust to noise and overfitting than cross-entropy.
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Diagnostic Uncertainty: Teaching Language Models to Describe Open-Ended Uncertainty
uncertainty hallucination trust large language model
Language models (LMs) often hallucinate. While uncertainty measures like calibration scores provide coarse measures of model uncertainty (e.g. "This proof is 40% likely to be correct"), ideally a model could tell us what it's uncertain about, such as "I don't know how to find the length of side AB," enabling people to understand exactly where to trust a model response.
We propose diagnostic uncertainty: open-ended descriptions of uncertainty that are grounded in model behavior. Our key idea is that a model can be said to be uncertain about X (e.g., "how to find the length of side AB") if its responses significantly improve after being told
X, and X is earliest in its reasoning process.
We implement a method to bootstrap models' ability to generate these diagnostic uncertainty descriptions by iteratively training on sampled descriptions that satisfy these criteria.
To evaluate whether diagnostic descriptions are meaningful, we provide the model with the information it claims to be uncertain about and measure whether its performance improves.
Compared to the descriptions generated by prompting alone, resolving diagnostic uncertainty descriptions leads to 8% higher accuracy and 20% more reduction in entropy of the answer distribution, supporting the hypothesis that diagnostic uncertainty is more faithful to the model's underlying uncertainty.
The main contribution of our work is a framework for operationalizing open-ended uncertainty in LMs, enabling richer ways for people to understand LM behavior beyond raw probabilities.
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Physics-Informed Machine Learning for Fluid Flow Prediction in Porous Media
Physics-Informed Machine Learning Fluid Flow Porous Media
The objective of this work is to predict grid-level flow fields in porous media as a priori to determining the permeability of porous media.
A physics-informed ML model is developed by using the results from numerical fluid flow simulations of randomly distributed circular grains to represent the porous media. The deep U-Net and ResNet neural network architectures are combined to train a deep learning model that avoids vanishing gradient issues.
The model integrates continuity and momentum conservation equations into the loss function to ensure physical consistency. Additionally, we modify the padding function in convolutional layers to use circular paddings, mimicking periodic boundary conditions in LB simulations. By learning inter-grid communications, the ML model achieves precise flow predictions for new simulation sets with high accuracy. The robustness of the developed model is then tested for numerous variations of porous media that have not been used for developing the model.
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Exploring Vision-Language Alignment Under Subtle Contradictions
Vision-language modeling adversarial attacks safety alignment.
Vision-language models (VLMs) have made notable progress in tasks such as object detection, scene interpretation, and cross-modal reasoning. However, they continue to face significant challenges when subjected to adversarial attacks. The simplicity of including hidden text in websites points to a critical need for a deeper understanding of how misleading text disrupts performance in multimodal applications. In this study, we systematically introduce faintly embedded and clearly visible contradictory text into a large-scale dataset, examining its effects on object counting, object detection, and scene description under varying text visibility. Our findings show that counting accuracy suffers significantly in the presence of adversarial textual perturbations, while object detection remains robust and scene descriptions exhibit only minor shifts under faint disruptions. These observations highlight the importance of building more resilient multimodal architectures that prioritize reliable visual signals and effectively handle subtle textual contradictions, ultimately enhancing trustworthiness in complex, real-world vision-language scenarios.
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GLUECode: A Benchmark for Source Code Machine Learning Models
benchmark source code code understanding deep learning
A multitude of machine learning models for source code have been proposed in the recent years capturing various aspects of the inherent rich structure and semantics of code. However, these models are commonly designed to perform well on a single task, failing to capture code's multifaceted nature. To address this, we present GLUECode, Global and Local Understanding Evaluation of Code, a benchmark of diverse tasks to evaluate machine learning models of source code.
Crucially, GLUECode accounts for the distinct characteristics of source code: (1) source code is highly structured and (2) source code is often composed of multiple interacting entities. Existing tasks incentivize researchers to create models and code representations that perform well on a single task - commonly focusing on local reasoning. GLUECode aims to allow researchers to experiment with multiple local and global source code representations, and evaluate these models on their ability to capture the diverse characteristics of source code, thus driving the community towards building robust source code models incorporating global reasoning.
We present results for several baselines. The GLUECode tasks are challenging for the evaluated baselines; no model achieves convincing performance across all tasks. This indicates that there is ample room for progress on GLUECode.
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Matrix Shuffle-Exchange Networks for Hard 2D Tasks
matrix network networks hard data tasks matrix main tools images
Convolutional neural networks have become the main tools for processing two-dimensional data. They work well for images, yet convolutions have a limited receptive field that prevents its applications to more complex 2D tasks. We propose a new neural model, called Matrix Shuffle-Exchange network, that can efficiently exploit long-range dependencies in 2D data and has comparable speed to a convolutional neural network. It is derived from Neural Shuffle-Exchange network and has $\mathcal{O}( \log{n})$ layers and $\mathcal{O}( n^2 \log{n})$ total time and space complexity for processing a $n \times n$ data matrix. We show that the Matrix Shuffle-Exchange network is well-suited for algorithmic and logical reasoning tasks on matrices and dense graphs, exceeding convolutional and graph neural network baselines. Its distinct advantage is the capability of retaining full long-range dependency modelling when generalizing to larger instances -- much larger than could be processed with models equipped with a dense attention mechanism.
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Can Transformers be Strong Treatment Effect Estimators?
Transformers Causal Inference
In this paper, we develop a general framework based on the Transformer architecture to address a variety of challenging treatment effect estimation (TEE) problems. Our methods are applicable both when covariates are tabular and when they consist of sequences (e.g., in text), and can handle discrete, continuous, structured, or dosage-associated treatments. While Transformers have already emerged as dominant methods for diverse domains, including natural language and computer vision, our experiments with Transformers as Treatment Effect Estimators (TransTEE) demonstrate that these inductive biases are also effective on the sorts of estimation problems and datasets that arise in research aimed at estimating causal effects. Moreover, we propose a propensity score network that is trained with TransTEE in an adversarial manner to promote independence between covariates and treatments to further address selection bias. Through extensive experiments, we show that TransTEE significantly outperforms competitive baselines with greater parameter efficiency over a wide range of benchmarks and settings.
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Learning Lagrangian Fluid Dynamics with Graph Neural Networks
particle hydrodynamics graph neural networks Lagrangian fluids
We present a data-driven model for fluid simulation under Lagrangian representation. Our model uses graphs to describe the fluid field, where physical quantities are encoded as node and edge features. Instead of directly predicting the acceleration or position correction given the current state, we decompose the simulation scheme into separate parts - advection, collision, and pressure projection. For these different reasoning tasks, we propose two kinds of graph neural network structures, node-focused networks, and edge-focused networks. By introducing physics prior knowledge, our model can be efficient in terms of training and inference. Our tests show that the learned model can produce accurate results and remain stable in scenarios with a large amount of particles and different geometries. Unlike many previous works, further tests demonstrate that our model is able to retain many important physical properties of incompressible fluids, such as minor divergence and reasonable pressure distribution. Additionally, our model can adopt a range of time step sizes different from ones using in the training set, which indicates its robust generalization capability.
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Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices
meta-reinforcement learning reinforcement learning exploration
The goal of meta-reinforcement learning (meta-RL) is to build agents that can quickly learn new tasks by leveraging prior experience on related tasks. Learning a new task often requires both exploring to gather task-relevant information and exploiting this information to solve the task. In principle, optimal exploration and exploitation can be learned end-to-end by simply maximizing task performance. However, such meta-RL approaches struggle with local optima due to a chicken-and-egg problem: learning to explore requires good exploitation to gauge the exploration's utility, but learning to exploit requires information gathered via exploration. Optimizing separate objectives for exploration and exploitation can avoid this problem, but prior meta-RL exploration objectives yield suboptimal policies that gather information irrelevant to the task. We alleviate both concerns by constructing an exploitation objective that automatically identifies task-relevant information and an exploration objective to recover only this information. This avoids local optima in end-to-end training, without sacrificing optimal exploration. Empirically, DREAM substantially outperforms existing approaches on complex meta-RL problems, such as sparse-reward 3D visual navigation.
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Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning
unsupervised reinforcement learning goal-conditioned policy intrinsic reward
It is of significance for an agent to learn a widely applicable and general-purpose policy that can achieve diverse goals including images and text descriptions. Considering such perceptually-specific goals, the frontier of deep reinforcement learning research is to learn a goal-conditioned policy without hand-crafted rewards. To learn this kind of policy, recent works usually take as the reward the non-parametric distance to a given goal in an explicit embedding space. From a different viewpoint, we propose a novel unsupervised learning approach named goal-conditioned policy with intrinsic motivation (GPIM), which jointly learns both an abstract-level policy and a goal-conditioned policy. The abstract-level policy is conditioned on a latent variable to optimize a discriminator and discovers diverse states that are further rendered into perceptually-specific goals for the goal-conditioned policy. The learned discriminator serves as an intrinsic reward function for the goal-conditioned policy to imitate the trajectory induced by the abstract-level policy. Experiments on various robotic tasks demonstrate the effectiveness and efficiency of our proposed GPIM method which substantially outperforms prior techniques.
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Saliency Grafting: Innocuous Attribution-Guided Mixup with Calibrated Label Mixing
Deep learning Data augmentation Input attribution
The Mixup scheme of mixing a pair of samples to create an augmented training sample has gained much attention recently for better training of neural networks. A straightforward and widely used extension is to combine Mixup and regional dropout methods: removing random patches from a sample and replacing it with the features from another sample. Albeit their simplicity and effectiveness, these methods are prone to create harmful samples due to their randomness. In recent studies, attempts to prevent such a phenomenon by selecting only the most informative features are gradually emerging. However, this maximum saliency strategy acts against their fundamental duty of sample diversification as they always deterministically select regions with maximum saliency, injecting bias into the augmented data. To address this problem, we present Saliency Grafting, a novel Mixup-like data augmentation method that captures the best of both ways. By stochastically sampling the features and ‘grafting’ them onto another sample, our method effectively generates diverse yet meaningful samples. The second ingredient of Saliency Grafting is to produce the label of the grafted sample by mixing the labels in a saliency-calibrated fashion, which rectifies supervision misguidance introduced by the random sampling procedure. Our experiments under CIFAR and ImageNet datasets show that our scheme outperforms the current state-of-the-art augmentation strategies not only in terms of classification accuracy, but is also superior in coping under stress conditions such as data corruption and data scarcity. The code will be released.
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Adversarial Environment Generation for Learning to Navigate the Web
Web Navigation Adversarial Environment Generation Web Environment Design Minimax Regret Adversary Auto Curriculum
Learning to autonomously navigate the web is a difficult sequential decision making task. The state and action spaces are large and combinatorial in nature, and successful navigation may require traversing several partially-observed pages. One of the bottlenecks of training web navigation agents is providing a learnable curriculum of training environments that can cover the large variety of real-world websites. Therefore, we propose using Adversarial Environment Generation (AEG) to generate challenging web environments in which to train reinforcement learning (RL) agents. We introduce a new benchmarking environment, gMiniWoB, which enables an RL adversary to use compositional primitives to learn to generate complex websites. To train the adversary, we present a new decoder-like architecture that can directly control the difficulty of the environment, and a new training technique Flexible b-PAIRED. Flexible b-PAIRED jointly trains the adversary and a population of navigator agents and incentivizes the adversary to generate ”just-the-right-challenge” environments by simultaneously learning two policies encoded in the adversary’s architecture. First, for its environment complexity choice (difficulty budget), the adversary is rewarded with the performance of the best-performing agent in the population. Second, for selecting the design elements the adversary learns to maximize the regret using the difference in capabilities of navigator agents in population (flexible regret). The results show that the navigator agent trained with Flexible b-PAIRED generalizes to new environments, significantly outperforms competitive automatic curriculum generation baselines—including a state-of-the-art RL web navigation approach and prior methods for minimax regret AEG—on a set of challenging unseen test environments that are order of magnitude more complex than the previous benchmarks. The navigator agent achieves more than 75% success rate on all tasks, yielding 4x higher success rate that the strongest baseline.
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Is This Written by AI?
AI Safety Plagiarism Detection Hash Encoding Document Similarity
With the rapid advancement of large language models (LLMs) and generative AI technology, a challenging issue has emerged: How can we determine whether an article on the internet was written by a real person or generated by a LLMs-based AI? As the barriers to training and inference of LLMs continue to lower, a vast number of AI-generated articles could enable an inexperienced person to cheat as an expert in a particular field. Traditional text plagiarism detection techniques can address this issue to some extent, but all of them have their own limitations. We provide a systematic review of existing text plagiarism detection methods and propose a new benchmark to evaluate the accuracy of various text detection techniques across different scenarios.
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Augmented Memory Networks for Streaming-Based Active One-Shot Learning
Active Learning Reinforcement Learning Few-Shot Learning
One of the major challenges in training deep architectures for predictive tasks is the scarcity and cost of labeled training data. Active Learning (AL) is one way of addressing this challenge. In stream-based AL, observations are continuously made available to the learner that have to decide whether to request a label or to make a prediction. The goal is to reduce the request rate while at the same time maximize prediction performance. In previous research, reinforcement learning has been used for learning the AL request/prediction strategy. In our work, we propose to equip a reinforcement learning process with memory augmented neural networks, to enhance the one-shot capabilities. Moreover, we introduce Class Margin Sampling (CMS) as an extension of the standard margin sampling to the reinforcement learning setting. This strategy aims to reduce training time and improve sample efficiency in the training process. We evaluate the proposed method on a classification task using empirical accuracy of label predictions and percentage of label requests. The results indicates that the proposed method, by making use of the memory augmented networks and CMS in the training process, outperforms existing baselines.
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Estimating Treatment Effects via Orthogonal Regularization
Treatment Effects Regularization Neural Networks
Decision-making often requires accurate estimation of causal effects from observational data. This is challenging as outcomes of alternative decisions are not observed and have to be estimated. Previous methods estimate outcomes based on unconfoundedness but neglect any constraints that unconfoundedness imposes on the outcomes. In this paper, we propose a novel regularization framework in which we formalize unconfoundedness as an orthogonality constraint. We provide theoretical guarantees that this yields an asymptotically normal estimator for the average causal effect. Compared to other estimators, its asymptotic variance is strictly smaller. Based on our regularization framework, we develop deep orthogonal networks for unconfounded treatments (DONUT) which learn outcomes that are orthogonal to the treatment assignment. Using a variety of benchmark datasets for causal inference, we demonstrate that DONUT outperforms the state-of-the-art substantially.
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Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning
Machine Unlearning Large Language Models Minority Groups
Large Language Models (LLMs) embed sensitive, human-generated data, prompting the need for unlearning methods. Although certified unlearning offers strong privacy guarantees, its restrictive assumptions make it unsuitable for LLMs, giving rise to various heuristic approaches typically assessed through empirical evaluations. These standard evaluations randomly select data for removal, apply unlearning techniques, and use membership inference attacks (MIAs) to compare unlearned models against models retrained without the removed data. However, to ensure robust privacy protections for every data point, it is essential to account for scenarios in which certain data subsets face elevated risks. Prior research suggests that outliers, particularly including data tied to minority groups, often exhibit higher memorization propensity which indicates they may be more difficult to unlearn. Building on these insights, we introduce a complementary, minority-aware evaluation framework to highlight blind spots in existing frameworks. We substantiate our findings with carefully designed experiments, using canaries with personally identifiable information (PII) to represent these minority subsets and demonstrate that they suffer at least 20\% higher privacy leakage across various unlearning methods, MIAs, datasets, and LLM scales. Our proposed minority-aware evaluation framework marks an essential step toward more equitable and comprehensive assessments of LLM unlearning efficacy.
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Layer-adaptive Sparsity for the Magnitude-based Pruning
network pruning layerwise sparsity magnitude-based pruning
Recent discoveries on neural network pruning reveal that, with a carefully chosen layerwise sparsity, a simple magnitude-based pruning achieves state-of-the-art tradeoff between sparsity and performance. However, without a clear consensus on ``how to choose,'' the layerwise sparsities are mostly selected algorithm-by-algorithm, often resorting to handcrafted heuristics or an extensive hyperparameter search. To fill this gap, we propose a novel importance score for global pruning, coined layer-adaptive magnitude-based pruning (LAMP) score; the score is a rescaled version of weight magnitude that incorporates the model-level $\ell_2$ distortion incurred by pruning, and does not require any hyperparameter tuning or heavy computation.
Under various image classification setups, LAMP consistently outperforms popular existing schemes for layerwise sparsity selection.
Furthermore, we observe that LAMP continues to outperform baselines even in weight-rewinding setups, while the connectivity-oriented layerwise sparsity (the strongest baseline overall) performs worse than a simple global magnitude-based pruning in this case. Code: https://github.com/jaeho-lee/layer-adaptive-sparsity
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