16 citations · 73 across the 29 of their papers we have counts for
4 papers · 1 filter
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…