12 citations · 12 across the 8 of their papers we have counts for
8 papers
Distributional Robustness of Linear Contracts
Shiliang Zuo
Linear contracts are ubiquitous in practice, yet optimal contract theory often prescribes complex, nonlinear structures. We provide a distributional robustness justification for li…
Learning is Revelation in Disguise: Optimal Regret and Equivalence Results for Dynamic Pricing
Shiliang Zuo
We study dynamic pricing where a seller repeatedly interacts with a strategic, non-myopic buyer who has a fixed private valuation and discounts future utility. Prior work focused e…
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…
Optimizing Contracts in Principal-Agent Team Production
Shiliang Zuo
We study a principal-agent team production model. The principal hires a team of agents to participate in a common production task. The exact effort of each agent is unobservable an…
Harnessing the Continuous Structure: Utilizing the First-order Approach in Online Contract Design
Shiliang Zuo
This work studies the online contract design problem. The principal's goal is to learn the optimal contract that maximizes her utility through repeated interactions, without prior…
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…