519 citations · 1.6k across the 49 of their papers we have counts for
50 papers
Langevin Dynamics: A Unified Perspective on Optimization via Lyapunov Potentials
August Y. Chen, Ayush Sekhari, Karthik Sridharan
We study the problem of non-convex optimization using Stochastic Gradient Langevin Dynamics (SGLD). SGLD is a natural and popular variation of stochastic gradient descent where at…
Online Learning with Unknown Constraints
Karthik Sridharan, Seung Won Wilson Yoo
We consider the problem of online learning where the sequence of actions played by the learner must adhere to an unknown safety constraint at every round. The goal is to minimize r…
Selective Sampling and Imitation Learning via Online Regression
Ayush Sekhari, Karthik Sridharan, Wen Sun +1
We consider the problem of Imitation Learning (IL) by actively querying noisy expert for feedback. While imitation learning has been empirically successful, much of prior work assu…
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent
Satyen Kale, Jason D. Lee, Chris De Sa +2
Stochastic Gradient Descent (SGD) has been the method of choice for learning large-scale non-convex models. While a general analysis of when SGD works has been elusive, there has b…
On the Complexity of Adversarial Decision Making
Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari +1
A central problem in online learning and decision making -- from bandits to reinforcement learning -- is to understand what modeling assumptions lead to sample-efficient learning g…
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation
Christoph Dann, Yishay Mansour, Mehryar Mohri +2
Myopic exploration policies such as epsilon-greedy, softmax, or Gaussian noise fail to explore efficiently in some reinforcement learning tasks and yet, they perform well in many o…