118 citations · 610 across the 55 of their papers we have counts for
6 papers · 2 filters
Sublinear Optimal Policy Value Estimation in Contextual Bandits
Weihao Kong, Gregory Valiant, Emma Brunskill
We study the problem of estimating the expected reward of the optimal policy in the stochastic disjoint linear bandit setting. We prove that for certain settings it is possible to…
Making AI Forget You: Data Deletion in Machine Learning
Antonio Ginart, Melody Y. Guan, Gregory Valiant +1
Intense recent discussions have focused on how to provide individuals with control over when their data can and cannot be used --- the EU's Right To Be Forgotten regulation is an e…
Sample Amplification: Increasing Dataset Size even when Learning is Impossible
Brian Axelrod, Shivam Garg, Vatsal Sharan +1
Given data drawn from an unknown distribution, , to what extent is it possible to ``amplify'' this dataset and output an even larger set of samples that appear to have been draw…
Implicit regularization for deep neural networks driven by an Ornstein-Uhlenbeck like process
Guy Blanc, Neha Gupta, Gregory Valiant +1
We consider networks, trained via stochastic gradient descent to minimize loss, with the training labels perturbed by independent noise at each iteration. We characterize…
Memory-Sample Tradeoffs for Linear Regression with Small Error
Vatsal Sharan, Aaron Sidford, Gregory Valiant
We consider the problem of performing linear regression over a stream of -dimensional examples, and show that any algorithm that uses a subquadratic amount of memory exhibits a…
A Theory of Selective Prediction
Mingda Qiao, Gregory Valiant
We consider a model of selective prediction, where the prediction algorithm is given a data sequence in an online fashion and asked to predict a pre-specified statistic of the upco…