3k citations · 24.2k across the 299 of their papers we have counts for
44 papers · 1 filter
On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces
Zhuoran Yang, Chi Jin, Zhaoran Wang +2
The classical theory of reinforcement learning (RL) has focused on tabular and linear representations of value functions. Further progress hinges on combining RL with modern functi…
Online Learning Demands in Max-min Fairness
Kirthevasan Kandasamy, Gur-Eyal Sela, Joseph E Gonzalez +2
We describe mechanisms for the allocation of a scarce resource among multiple users in a way that is efficient, fair, and strategy-proof, but when users do not know their resource…
Optimal Mean Estimation without a Variance
Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett +1
We study the problem of heavy-tailed mean estimation in settings where the variance of the data-generating distribution does not exist. Concretely, given a sample $\mathbf{X} = \{X…
Stochastic Approximation for Online Tensorial Independent Component Analysis
Chris Junchi Li, Michael I. Jordan
Independent component analysis (ICA) has been a popular dimension reduction tool in statistical machine learning and signal processing. In this paper, we present a convergence anal…
Do Offline Metrics Predict Online Performance in Recommender Systems?
Karl Krauth, Sarah Dean, Alex Zhao +4
Recommender systems operate in an inherently dynamical setting. Past recommendations influence future behavior, including which data points are observed and how user preferences ch…
Post-Selection Inference via Algorithmic Stability
Tijana Zrnic, Michael I. Jordan
When the target of statistical inference is chosen in a data-driven manner, the guarantees provided by classical theories vanish. We propose a solution to the problem of inference…