12 citations · 12 across the 8 of their papers we have counts for
5 papers · 1 filter
Online Learning with Ordered Experts (via Two-point Feedback)
Shiliang Zuo
We study adversarial online learning with expert recommendations in a totally ordered action space, where the experts' recommendations may change and be reordered arbitrarily. Our…
Greedy Algorithm for Structured Bandits: A Sharp Characterization of Asymptotic Success / Failure
Aleksandrs Slivkins, Yunzong Xu, Shiliang Zuo
We study the greedy (exploitation-only) algorithm in bandit problems with a known reward structure. We allow arbitrary finite reward structures, while prior work focused on a few s…
Learning Under Moral Hazard with Instrumental Regression and Generalized Method of Moments
Shiliang Zuo
Machine learning has become increasingly popular in informing data-driven policy-making. Policies influence behavior in individuals or populations, and ideally, through observation…
Contextual Bandits with Online Neural Regression
Rohan Deb, Yikun Ban, Shiliang Zuo +2
Recent works have shown a reduction from contextual bandits to online regression under a realizability assumption [Foster and Rakhlin, 2020, Foster and Krishnamurthy, 2021]. In thi…
Near Optimal Adversarial Attacks on Stochastic Bandits and Defenses with Smoothed Responses
Shiliang Zuo
I study adversarial attacks against stochastic bandit algorithms. At each round, the learner chooses an arm, and a stochastic reward is generated. The adversary strategically adds…