26 citations · 40 across the 4 of their papers we have counts for
8 papers
Towards Painless Policy Optimization for Constrained MDPs
Arushi Jain, Sharan Vaswani, Reza Babanezhad +2
We study policy optimization in an infinite horizon, -discounted constrained Markov decision process (CMDP). Our objective is to return a policy that achieves large expected rew…
SVRG Meets AdaGrad: Painless Variance Reduction
Benjamin Dubois-Taine, Sharan Vaswani, Reza Babanezhad +2
Variance reduction (VR) methods for finite-sum minimization typically require the knowledge of problem-dependent constants that are often unknown and difficult to estimate. To addr…
Geometry-Aware Universal Mirror-Prox
Reza Babanezhad, Simon Lacoste-Julien
Mirror-prox (MP) is a well-known algorithm to solve variational inequality (VI) problems. VI with a monotone operator covers a large group of settings such as convex minimization,…
Reducing the variance in online optimization by transporting past gradients
Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad +2
Most stochastic optimization methods use gradients once before discarding them. While variance reduction methods have shown that reusing past gradients can be beneficial when there…
Semantics Preserving Adversarial Learning
Ousmane Amadou Dia, Elnaz Barshan, Reza Babanezhad
While progress has been made in crafting visually imperceptible adversarial examples, constructing semantically meaningful ones remains a challenge. In this paper, we propose a fra…
M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning
Issam Laradji, Reza Babanezhad
Unsupervised domain adaptation techniques have been successful for a wide range of problems where supervised labels are limited. The task is to classify an unlabeled `target' datas…