misovalko/my-research-papers

Michal Valko Research Papers

Selected research papers by Michal Valko on bandits, reinforcement learning, and online learning.

Papers

• Spectral bandits for smooth graph functions with applications in recommender systems
• Conditional anomaly detection methods for patient-management alert systems
• Learning predictive models for combinations of heterogeneous proteomic data sources
• Outlier detection for patient monitoring and alerting
• Conditional outlier detection for clinical alerting
• Evidence-based anomaly detection in clinical domains
• Feature importance analysis for patient management decisions
• Bandits on graphs and structures
• Adaptive graph-based algorithms for conditional anomaly detection and semi-supervised learning
• Bandits attack function optimization
• Active multiple matrix completion with adaptive confidence sets
• Middle-mile logistics through the lens of goal-conditioned reinforcement learning
• Black-box optimization of noisy functions with unknown smoothness
• Trading off rewards and errors in multi-armed bandits
• Revealing graph bandits for maximizing local influence
• Distance metric learning for conditional anomaly detection
• Bayesian policy gradient and actor-critic algorithms
• Online semi-supervised perception: Real-time learning without explicit feedback
• Learning from a single labeled face and a stream of unlabeled data
• Semi-supervised learning with max-margin graph cuts
• Evolutionary feature selection for spiking neural network pattern classifiers
• Large-scale semi-supervised learning with online spectral graph sparsification
• Online learning with Erdős-Rényi side-observation graphs
• Online combinatorial optimization with stochastic decision sets and adversarial losses
• Spectral bandits
• Efficient learning by implicit exploration in bandit problems with side observations
• Extreme bandits
• Stochastic simultaneous optimistic optimization
• Conditional anomaly detection using soft harmonic functions: An application to clinical alerting
• Pliable rejection sampling
• Pack only the essentials: Adaptive dictionary learning for kernel ridge regression
• Conditional anomaly detection with soft harmonic functions
• A single algorithm for both restless and rested rotting bandits
• Maximum entropy semi-supervised inverse reinforcement learning
• Analysis of Nyström method with sequential ridge leverage scores
• Improved large-scale graph learning through ridge spectral sparsification
• Planning in entropy-regularized Markov decision processes and games
• On two ways to use determinantal point processes for Monte Carlo integration
• Budgeted online influence maximization