174 citations · 751 across the 34 of their papers we have counts for
11 papers · 1 filter
Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems
Dhruv Malik, Ashwin Pananjady, Kush Bhatia +3
We study derivative-free methods for policy optimization over the class of linear policies. We focus on characterizing the convergence rate of these methods when applied to linear-…
Gen-Oja: A Two-time-scale approach for Streaming CCA
Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion +2
In this paper, we study the problems of principal Generalized Eigenvector computation and Canonical Correlation Analysis in the stochastic setting. We propose a simple and efficien…
Rademacher Complexity for Adversarially Robust Generalization
Dong Yin, Kannan Ramchandran, Peter Bartlett
Many machine learning models are vulnerable to adversarial attacks; for example, adding adversarial perturbations that are imperceptible to humans can often make machine learning m…
A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption
Peter L. Bartlett, Victor Gabillon, Michal Valko
We study the problem of optimizing a function under a \emph{budgeted number of evaluations}. We only assume that the function is \emph{locally} smooth around one of its global opti…
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning
Dong Yin, Yudong Chen, Kannan Ramchandran +1
We study robust distributed learning that involves minimizing a non-convex loss function with saddle points. We consider the Byzantine setting where some worker machines have abnor…
Best of many worlds: Robust model selection for online supervised learning
Vidya Muthukumar, Mitas Ray, Anant Sahai +1
We introduce algorithms for online, full-information prediction that are competitive with contextual tree experts of unknown complexity, in both probabilistic and adversarial setti…