Publications

Preprints

• A Machine Learning Theory Perspective on Strategic Litigation
  Melissa Dutz, Han Shao, Avrim Blum, and Aloni Cohen
  arXiv, 2025
• 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

• Probably Approximately Precision and Recall Learning
  (α–β) Lee Cohen, Yishay Mansour, Shay Moran, and Han Shao
  NeurIPS, 2025
• How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension
  (α–β) Cynthia Dwork, Lunjia Hu, and Han Shao
  NeurIPS, 2025
• 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, 2022Oral
• 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