160 citations · 1.5k across the 122 of their papers we have counts for
12 papers · 1 filter
Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded Span
Woojin Chae, Kihyuk Hong, Yufan Zhang +2
This paper proposes a computationally tractable algorithm for learning infinite-horizon average-reward linear mixture Markov decision processes (MDPs) under the Bellman optimality…
On the Benefits of Active Data Collection in Operator Learning
Unique Subedi, Ambuj Tewari
We study active data collection strategies for operator learning when the target operator is linear and the input functions are drawn from a mean-zero stochastic process with conti…
Generation through the lens of learning theory
Jiaxun Li, Vinod Raman, Ambuj Tewari
We study generation through the lens of statistical learning theory. First, we abstract and formalize the results of Gold [1967], Angluin [1979], Angluin [1980] and Kleinberg and M…
Latency-Aware Contextual Bandit: Application to Cryo-EM Data Collection
Lai Wei, Ambuj Tewari, Michael A. Cianfrocco
We introduce a latency-aware contextual bandit framework that generalizes the standard contextual bandit problem, where the learner adaptively selects arms and switches decision se…
Controlling Statistical, Discretization, and Truncation Errors in Learning Fourier Linear Operators
Unique Subedi, Ambuj Tewari
We study learning-theoretic foundations of operator learning, using the linear layer of the Fourier Neural Operator architecture as a model problem. First, we identify three main e…
Smoothed Online Classification can be Harder than Batch Classification
Vinod Raman, Unique Subedi, Ambuj Tewari
We study online classification under smoothed adversaries. In this setting, at each time point, the adversary draws an example from a distribution that has a bounded density with r…