212 citations · 419 across the 46 of their papers we have counts for
7 papers · 1 filter
A Statistical Analysis of Polyak-Ruppert Averaged Q-learning
Xiang Li, Wenhao Yang, Jiadong Liang +2
We study Q-learning with Polyak-Ruppert averaging in a discounted Markov decision process in synchronous and tabular settings. Under a Lipschitz condition, we establish a functiona…
Parallelizing Contextual Bandits
Jeffrey Chan, Aldo Pacchiano, Nilesh Tripuraneni +3
Standard approaches to decision-making under uncertainty focus on sequential exploration of the space of decisions. However, \textit{simultaneously} proposing a batch of decisions,…
Interleaving Computational and Inferential Thinking: Data Science for Undergraduates at Berkeley
Ani Adhikari, John DeNero, Michael I. Jordan
The undergraduate data science curriculum at the University of California, Berkeley is anchored in five new courses that emphasize computational thinking, inferential thinking, and…
Multi-Source Causal Inference Using Control Variates
Wenshuo Guo, Serena Wang, Peng Ding +2
While many areas of machine learning have benefited from the increasing availability of large and varied datasets, the benefit to causal inference has been limited given the strong…
On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective
Tyler Westenbroek, Max Simchowitz, Michael I. Jordan +1
%!TEX root = LCSS_main_max.tex The widespread adoption of nonlinear Receding Horizon Control (RHC) strategies by industry has led to more than 30 years of intense research efforts…
A Variational Inequality Approach to Bayesian Regression Games
Wenshuo Guo, Michael I. Jordan, Tianyi Lin
Bayesian regression games are a special class of two-player general-sum Bayesian games in which the learner is partially informed about the adversary's objective through a Bayesian…
Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data
Esther Rolf, Theodora Worledge, Benjamin Recht +1
Collecting more diverse and representative training data is often touted as a remedy for the disparate performance of machine learning predictors across subpopulations. However, a…