Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Global Optima of Kernelized Adversarial Representation Learning   Adversarial representation learning is a promising paradigm for obtaining
data representations that are invariant to certain sensitive attributes while
retaining the information necessary for predicting target attributes. Existing
approaches solve this problem through iterative adversarial minimax
optimization and lack theoretical guarantees. In this paper, we first study the
"linear" form of this problem i.e., the setting where all the players are
linear functions. We show that the resulting optimization problem is both
non-convex and non-differentiable. We obtain an exact closed-form expression
for its global optima through spectral learning and provide performance
guarantees in terms of analytical bounds on the achievable utility and
invariance. We then extend this solution and analysis to non-linear functions
through kernel representation. Numerical experiments on UCI, Extended Yale B
and CIFAR-100 datasets indicate that, (a) practically, our solution is ideal
for "imparting" provable invariance to any biased pre-trained data
representation, and (b) empirically, the trade-off between utility and
invariance provided by our solution is comparable to iterative minimax
optimization of existing deep neural network based approaches. Code is
available at https://github.com/human-analysis/Kernel-ARL

22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an