Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs. - Han Shao 001; Xiaotian Yu; Irwin King; Michael R. Lyu (NeurIPS, 2018)

Pure Exploration of Multi-Armed Bandits with Heavy-Tailed Payoffs. - Xiaotian Yu; Han Shao 001; Michael R. Lyu; Irwin King (UAI, 2018)

Structure Adaptive Algorithms for Stochastic Bandits. - Rémy Degenne; Han Shao 001; Wouter M. Koolen (ICML, 2020)

Online Learning with Primary and Secondary Losses. - Avrim Blum; Han Shao 001 (NeurIPS, 2020)

Robust learning under clean-label attack. - Avrim Blum; Steve Hanneke; Jian Qian; Han Shao 001 (COLT, 2021)

One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning. - Avrim Blum; Nika Haghtalab; Richard Lanas Phillips; Han Shao 001 (ICML, 2021)

A Theory of PAC Learnability under Transformation Invariances. - Han Shao 001; Omar Montasser; Avrim Blum (NeurIPS, 2022)

Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback. - Han Shao 001; Lee Cohen 001; Avrim Blum; Yishay Mansour; Aadirupa Saha; Matthew R. Walter (NeurIPS, 2023)

Strategic Classification under Unknown Personalized Manipulation. - Han Shao 001; Avrim Blum; Omar Montasser (NeurIPS, 2023)

On the Effect of Defections in Federated Learning and How to Prevent Them. - Minbiao Han; Kumar Kshitij Patel; Han Shao 001; Lingxiao Wang (CoRR, 2023)

Learnability Gaps of Strategic Classification. - Lee Cohen 001; Yishay Mansour; Shay Moran; Han Shao 001 (COLT, 2024)

Incentivized Collaboration in Active Learning. - Lee Cohen 001; Han Shao 001 (FORC, 2024)

Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization. - Omar Montasser; Han Shao 001; Emmanuel Abbe (NeurIPS, 2024)

Efficient Prior-Free Mechanisms for No-Regret Agents. - Natalie Collina; Aaron Roth 001; Han Shao 001 (EC, 2024)

Trustworthy Machine Learning under Social and Adversarial Data Sources. - Han Shao 001 (CoRR, 2024)

Should Decision-Makers Reveal Classifiers in Online Strategic Classification? - Han Shao 001; Shuo Xie; Kunhe Yang (ICML, 2025)

Probably Approximately Precision and Recall Learning. - Lee Cohen 001; Yishay Mansour; Shay Moran; Han Shao 001 (NeurIPS, 2025)

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension. - Cynthia Dwork; Lunjia Hu; Han Shao 001 (NeurIPS, 2025)

Incentives in Federated Learning with Heterogeneous Agents. - Ariel D. Procaccia; Han Shao 001; Itai Shapira (CoRR, 2025)

A Machine Learning Theory Perspective on Strategic Litigation. - Melissa Dutz; Han Shao 001; Avrim Blum; Aloni Cohen (FORC, 2026)

On Randomized Algorithms in Online Strategic Classification. - Chase Hutton; Adam Melrod; Han Shao 001 (CoRR, 2026)

Equitable Evaluation via Elicitation. - Elbert Du; Cynthia Dwork; Lunjia Hu; Reid McIlroy-Young; Han Shao 001; Linjun Zhang (CoRR, 2026)

A Theoretical Framework for Statistical Evaluability of Generative Models. - Shashaank Aiyer; Yishay Mansour; Shay Moran; Han Shao 001 (CoRR, 2026)

Online Set Learning from Precision and Recall Feedback. - Lee Cohen 001; Yishay Mansour; Shay Moran; Han Shao 001 (CoRR, 2026)

Scale-Sensitive Shattering: Learnability and Evaluability at Optimal Scale. - Shashaank Aiyer; Yishay Mansour; Shay Moran; Han Shao 001; Tom Waknine (CoRR, 2026)