989 citations · 6.1k across the 184 of their papers we have counts for
10 papers · 2 filters
Optimal Estimation of Change in a Population of Parameters
Ramya Korlakai Vinayak, Weihao Kong, Sham M. Kakade
Paired estimation of change in parameters of interest over a population plays a central role in several application domains including those in the social sciences, epidemiology, me…
The Nonstochastic Control Problem
Elad Hazan, Sham M. Kakade, Karan Singh
We consider the problem of controlling an unknown linear dynamical system in the presence of (nonstochastic) adversarial perturbations and adversarial convex loss functions. In con…
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?
Simon S. Du, Sham M. Kakade, Ruosong Wang +1
Modern deep learning methods provide effective means to learn good representations. However, is a good representation itself sufficient for sample efficient reinforcement learning?…
Meta-Learning with Implicit Gradients
Aravind Rajeswaran, Chelsea Finn, Sham Kakade +1
A core capability of intelligent systems is the ability to quickly learn new tasks by drawing on prior experience. Gradient (or optimization) based meta-learning has recently emerg…
On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift
Alekh Agarwal, Sham M. Kakade, Jason D. Lee +1
Policy gradient methods are among the most effective methods in challenging reinforcement learning problems with large state and/or action spaces. However, little is known about ev…
Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal
Alekh Agarwal, Sham Kakade, Lin F. Yang
This work considers the sample and computational complexity of obtaining an -optimal policy in a discounted Markov Decision Process (MDP), given only access to a generative mode…
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares
Rong Ge, Sham M. Kakade, Rahul Kidambi +1
Minimax optimal convergence rates for classes of stochastic convex optimization problems are well characterized, where the majority of results utilize iterate averaged stochastic g…
Online Control with Adversarial Disturbances
Naman Agarwal, Brian Bullins, Elad Hazan +2
We study the control of a linear dynamical system with adversarial disturbances (as opposed to statistical noise). The objective we consider is one of regret: we desire an online c…
On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points
Chi Jin, Praneeth Netrapalli, Rong Ge +2
Gradient descent (GD) and stochastic gradient descent (SGD) are the workhorses of large-scale machine learning. While classical theory focused on analyzing the performance of these…
Provably Efficient Maximum Entropy Exploration
Elad Hazan, Sham M. Kakade, Karan Singh +1
Suppose an agent is in a (possibly unknown) Markov Decision Process in the absence of a reward signal, what might we hope that an agent can efficiently learn to do? This work studi…