519 citations · 2.8k across the 80 of their papers we have counts for
17 papers · 1 filter
Online Estimation via Offline Estimation: An Information-Theoretic Framework
Dylan J. Foster, Yanjun Han, Jian Qian +1
The classical theory of statistical estimation aims to estimate a parameter of interest under data generated from a fixed design ("offline estimation"), while the contemporary t…
On the Performance of Empirical Risk Minimization with Smoothed Data
Adam Block, Alexander Rakhlin, Abhishek Shetty
In order to circumvent statistical and computational hardness results in sequential decision-making, recent work has considered smoothed online learning, where the distribution of…
Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making
Adam Block, Alexander Rakhlin, Max Simchowitz
Smoothed online learning has emerged as a popular framework to mitigate the substantial loss in statistical and computational complexity that arises when one moves from classical t…
Smoothed Online Learning is as Easy as Statistical Learning
Adam Block, Yuval Dagan, Noah Golowich +1
Much of modern learning theory has been split between two regimes: the classical offline setting, where data arrive independently, and the online setting, where data arrive adversa…
Intrinsic Dimension Estimation Using Wasserstein Distances
Adam Block, Zeyu Jia, Yury Polyanskiy +1
It has long been thought that high-dimensional data encountered in many practical machine learning tasks have low-dimensional structure, i.e., the manifold hypothesis holds. A natu…
Top- eXtreme Contextual Bandits with Arm Hierarchy
Rajat Sen, Alexander Rakhlin, Lexing Ying +4
Motivated by modern applications, such as online advertisement and recommender systems, we study the top- extreme contextual bandits problem, where the total number of arms can…