Han Shao
Email: [first name] at ttic.edu.
Hi! I am a CMSA postdoc at Harvard, working with Cynthia Dwork and Ariel Procaccia. Starting in fall 2025, I will be an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park (UMD). Before that, I did my PhD at TTIC, where I was extremely fortunate to be advised by Avrim Blum.
I am hiring PhD students at UMD CS. If you're interested, apply to the program and mention me as the faculty member of interest. If your interests and experience strongly align with mine, please feel free to email me your CV, research interests, and how your background connects with my research.
My primary research centers on machine learning theory, with a specific focus on modeling human strategic and adversarial behaviors within the learning process. I aim to understand how these behaviors affect machine learning systems and develop methods to enhance accuracy/robustness. Additionally, I am interested in gaining a theoretical understanding of empirical observations concerning adversarial robustness.
Publications
(α-β denotes alphabetical ordering, * denotes equal contribution)
Preprints
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
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.
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.
Awards
EECS Rising Star, Georgia Tech, 2023
Rising Star in Machine Learning, University of Maryland, 2023
Services
Co-organizer of the TTIC Student Workshop 2024.
Student member of TTIC DEI committee (2023-2024).