Search Results for author:
Found 4 papers, 3 papers with code
Adversarial Representation Learning With Closed-Form Solvers
Adversarial representation learning aims to learn data representations for a target task while removing unwanted sensitive information at the same time.
On Characterizing the Trade-off in Invariant Representation Learning
Solutions to invariant representation learning (IRepL) problems lead to a trade-off between utility and invariance when they are competing.
On the Global Optima of Kernelized Adversarial Representation Learning
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.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
