α-β denotes alphabetical ordering. * denotes equal contribution.

Preprints

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension

(α-β) Cynthia Dwork, Lunjia Hu, and Han Shao

arxiv, 2025.

A Machine Learning Theory Perspective on Strategic Litigation

Melissa Dutz, Han Shao, Avrim Blum, and Aloni Cohen

arxiv, 2025.

Probably Approximately Precision and Recall Learning

(α-β) Lee Cohen, Yishay Mansour, Shay Moran, and Han Shao

arxiv, 2024.

On the Effect of Defections in Federated Learning and How to Prevent Them

(α-β) Minbiao Han, Kumar Kshitij Patel, Han Shao, and Lingxiao Wang

arxiv, 2023.

Conference Papers

Should Decision-Makers Reveal Classifiers in Online Strategic Classification?

(α-β) Han Shao, Shuo Xie, and Kunhe Yang

ICML, 2025.

Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization

Omar Montasser, Han Shao, and Emmanuel Abbe

NeurIPS, 2024.

Efficient Prior-Free Mechanisms for No-Regret Agents

(α-β) Natalie Collina, Aaron Roth, and Han Shao

EC, 2024.

Learnability Gaps of Strategic Classification

(α-β) Lee Cohen, Yishay Mansour, Shay Moran, and Han Shao

COLT, 2024.

Incentivized Collaboration in Active Learning

(α-β) Lee Cohen and Han Shao

FORC, 2024.

Strategic Classification under Unknown Personalized Manipulation

Han Shao, Avrim Blum and Omar Montasser

NeurIPS, 2023.

Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback

Han Shao, Lee Cohen, Avrim Blum, Yishay Mansour, Aadirupa Saha and Matthew R. Walter

NeurIPS, 2023.

A Theory of PAC Learnability under Transformation Invariances

Han Shao, Omar Montasser and Avrim Blum

NeurIPS, 2022. (Oral)

Robust Learning under Clean-Label Attack

(α-β) Avrim Blum, Steve Hanneke, Jian Qian and Han Shao

COLT,  2021. 

One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning

(α-β) Avrim Blum, Nika Haghtalab, Richard Lanas Phillips and Han Shao

ICML, 2021

Online learning with preliminary and secondary losses

(α-β) Avrim Blum and Han Shao

NeurIPS, 2020.

Structure adaptive algorithms for stochastic bandits

Rémy Degenne, Han Shao and Wouter M. Koolen

ICML, 2020.

Almost optimal algorithms for linear stochastic bandits with heavy-tailed payoffs

Han Shao*, Xiaotian Yu*, Irwin King, and Michael R. Lyu

NeurIPS, 2018. (Spotlight)

Pure exploration of multi-armed bandits with heavy-tailed payoffs

Xiaotian Yu, Han Shao, Michael R. Lyu, and Irwin King

UAI, 2018.