6 citations · 6 across the 2 of their papers we have counts for
8 papers
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs
Naman Agarwal, Syomantak Chaudhuri, Prateek Jain +2
Q-learning is a popular Reinforcement Learning (RL) algorithm which is widely used in practice with function approximation (Mnih et al., 2015). In contrast, existing theoretical re…
A law of robustness for two-layers neural networks
Sébastien Bubeck, Yuanzhi Li, Dheeraj Nagaraj
We initiate the study of the inherent tradeoffs between the size of a neural network and its robustness, as measured by its Lipschitz constant. We make a precise conjecture that, f…
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms
Guy Bresler, Prateek Jain, Dheeraj Nagaraj +2
We study the problem of least squares linear regression where the data-points are dependent and are sampled from a Markov chain. We establish sharp information theoretic minimax lo…
Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth
Guy Bresler, Dheeraj Nagaraj
We prove sharp dimension-free representation results for neural networks with ReLU layers under square loss for a class of functions defined in the paper. These…
A Corrective View of Neural Networks: Representation, Memorization and Learning
Guy Bresler, Dheeraj Nagaraj
We develop a corrective mechanism for neural network approximation: the total available non-linear units are divided into multiple groups and the first group approximates the funct…
Phase Transitions for Detecting Latent Geometry in Random Graphs
Matthew Brennan, Guy Bresler, Dheeraj Nagaraj
Random graphs with latent geometric structure are popular models of social and biological networks, with applications ranging from network user profiling to circuit design. These g…