160 citations · 1.5k across the 122 of their papers we have counts for
9 papers · 1 filter
Offline Policy Evaluation and Optimization under Confounding
Chinmaya Kausik, Yangyi Lu, Kevin Tan +3
Evaluating and optimizing policies in the presence of unobserved confounders is a problem of growing interest in offline reinforcement learning. Using conventional methods for offl…
Learning Mixtures of Markov Chains and MDPs
Chinmaya Kausik, Kevin Tan, Ambuj Tewari
We present an algorithm for learning mixtures of Markov chains and Markov decision processes (MDPs) from short unlabeled trajectories. Specifically, our method handles mixtures of…
Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits
Sunrit Chakraborty, Saptarshi Roy, Ambuj Tewari
We consider the stochastic linear contextual bandit problem with high-dimensional features. We analyze the Thompson sampling algorithm using special classes of sparsity-inducing pr…
On Proper Learnability between Average- and Worst-case Robustness
Vinod Raman, Unique Subedi, Ambuj Tewari
Recently, Montasser et al. [2019] showed that finite VC dimension is not sufficient for proper adversarially robust PAC learning. In light of this hardness, there is a growing effo…
Online Agnostic Multiclass Boosting
Vinod Raman, Ambuj Tewari
Boosting is a fundamental approach in machine learning that enjoys both strong theoretical and practical guarantees. At a high-level, boosting algorithms cleverly aggregate weak le…
Adaptive Sampling for Discovery
Ziping Xu, Eunjae Shim, Ambuj Tewari +1
In this paper, we study a sequential decision-making problem, called Adaptive Sampling for Discovery (ASD). Starting with a large unlabeled dataset, algorithms for ASD adaptively l…