Publications
(* for equal contribution; (α-β) for alphabetical ordering; papers with purple titles are not on arXiv yet)
Selected Preprints
Ask to Learn: Query-Based Collaborative Reinforcement Learning
Kaicheng Zhang, Hongqian Niu, Laixi Shi, Didong Li, Guannan Qu, Zhun Deng
Incorporating LLM Embeddings for Variation Across the Human Genome
Hongqian Niu, Jordan G. Bryan, Jacob Williams, Hufeng Zhou, Zhun Deng, Haoyu Zhang, Xihao Li, Didong Li
An Economic Solution to Copyright Challenges of Generative AI
Jiachen T. Wang, Zhun Deng, Hiroaki Chiba-Okabe, Boaz Barak, Weijie J. Su
Media coverage: TechCrunch; MarkTechPost; ALLi; Harvard Ash Center (policy discussion).
Provable Pluralistic Alignment: Multi-Party RLHF under Offline Human Feedback
Huiying Zhong, Tianwei Gao, Zhiwei Steven Wu, Linjun Zhang, Weijie J. Su, Zhun Deng
Conference Proceedings
2026
OmniMechanism Design for Human-AI Collaboration with Impressionable Minds NeurIPS 2026 (accepted)
Tianwei Gao, Yifei Pang, Lunjia Hu, Kate Donahue, Lihua Lei, Vahid Tarokh, Zhun Deng
Rubrics as an Attack Surface: Stealthy Preference Drift in LLM Judges COLM 2026
Ruomeng Ding*, Yifei Pang*, He Sun, Yizhong Wang, Zhiwei Steven Wu, Zhun Deng
Zhangyi Liu*, Huaizhi Qu*, Xiaowei Yin*, He Sun, Yanjun Han, Tianlong Chen, Zhun Deng
Whom to Query for What: Adaptive Group Elicitation via Multi-Turn LLM Interactions ICML 2026
Ruomeng Ding*, Tianwei Gao*, Thomas P. Zollo, Eitan Bachmat, Richard Zemel, Zhun Deng
Catherine Chen, Jingyan Shen, Zhun Deng, Lihua Lei
2025
Performative Risk Control: Calibrating Models for Reliable Deployment under Performativity NeurIPS 2025
Victor Li, Baiting Chen, Yuzhen Mao, Qi Lei, Zhun Deng
Statistical Inference under Performativity NeurIPS 2025
Xiang Li*, Yunai Li*, Huiying Zhong*, Lihua Lei, Zhun Deng
Synergistic Weak-Strong Collaboration by Aligning Preferences ACL 2025
Yizhu Jiao, Xuchao Zhang, Zhaoyang Wang, Yubo Ma, Zhun Deng, Rujia Wang, Chetan Bansal, Saravan Rajmohan, Jiawei Han, Huaxiu Yao
QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions ICML 2025
Zhun Deng*, Tom Zollo*, Ben Eyre*, Amogh Inamdar, David Madras, Richard Zemel
Conformal Tail Risk Control for Large Language Model Alignment ICML 2025
Catherine Chen, Jingyan Shen, Zhun Deng, Lihua Lei
2024
Learning and Forgetting Unsafe Examples in Large Language Models ICML 2024
Jiachen Zhao, Zhun Deng, David Madras, James Zou, Mengye Ren
Media coverage: MarkTechPost.
Analyzing and Mitigating Object Hallucination in Large Vision-Language Models ICLR 2024
Yiyang Zhou*, Chenhang Cui*, Jaehong Yoon, Linjun Zhang, Zhun Deng, Chelsea Finn, Mohit Bansal, Huaxiu Yao
Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models ICLR 2024
Thomas P. Zollo, Todd Morrill*, Zhun Deng*, Jake C. Snell, Toniann Pitassi, Richard Zemel
2023
Distribution-free Statistical Dispersion Control for Societal Applications NeurIPS (spotlight, top 3% among submissions) 2023
Zhun Deng, Thomas P. Zollo, Jake C. Snell, Toniann Pitassi, Richard Zemel
PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification NeurIPS 2023
Qianli Shen, Wai Hoh Tang, Zhun Deng, Apostolos Psaros, Kenji Kawaguchi
Decision-Aware Conditional GANs for Time Series Data ICAIF (oral presentation) 2023
He Sun, Zhun Deng, Hui Chen, David Parkes
How Does Information Bottleneck Help Deep Learning? ICML 2023
Kenji Kawaguchi*, Zhun Deng*, Xu Ji*, Jiaoyang Huang
Reinforcement Learning with Stepwise Fairness Constraints AISTATS 2023
Zhun Deng, He Sun, Zhiwei Steven Wu, Linjun Zhang, David C. Parkes
Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data AISTATS 2023
Ryumei Nakada, Halil Ibrahim Gulluk, Zhun Deng, Wenlong Ji, James Zou, Linjun Zhang
FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data ICLR 2023
Zhun Deng, Jiayao Zhang, Linjun Zhang, Ting Ye, Yates Coley, Weijie Su, James Zou
Quantile Risk Control: a Flexible Framework for Bounding the Probability of High-loss Predictions ICLR 2023
Jake C. Snell, Thomas P. Zollo, Zhun Deng, Toniann Pitassi, Richard Zemel
Happymap: A Generalized Multi-Calibration Method ITCS 2023
(α-β) Zhun Deng, Cynthia Dwork, Linjun Zhang
2022
When and How Mixup Improves Calibration ICML 2022
Linjun Zhang*, Zhun Deng*, Kenji Kawaguchi, James Zou
Media coverage: Zeta Alpha (automated conference overview).
Robustness Implies Generalization via Data-dependent Generalization Bounds ICML (long presentation, top 2% among submissions) 2022
Kenji Kawaguchi, Zhun Deng, Kyle Luh, Jiaoyang Huang
An Unconstrained Layer-Peeled Perspective on Neural Collapse ICLR 2022
Wenlong Ji, Yiping Lu, Yiliang Zhang, Zhun Deng, Weijie Su
2021
Adversarial Training Helps Transfer Learning via Better Representations NeurIPS 2021
Zhun Deng*, Linjun Zhang*, Kailas Vodrahalli, Kenji Kawaguchi, James Zou
Toward Better Generalization Bounds with Locally Elastic Stability ICML 2021
Zhun Deng, Hangfeng He, Weijie Su
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data AISTATS (oral presentation, top 3% among submissions) 2021
Zhun Deng*, Linjun Zhang*, Amirata Ghorbani, James Zou
How Does Mixup Help With Robustness and Generalization? ICLR (spotlight, top 5% among submissions) 2021
Linjun Zhang*, Zhun Deng*, Kenji Kawaguchi*, Amirata Ghorbani, James Zou
Media coverage: Two Sigma (research highlight).
The Role of Gradient Noise in the Optimization of Neural Networks IEEE Big Data 2021
(α-β) Zhun Deng, Jiaoyang Huang, Kenji Kawaguchi
2020
Interpreting Robust Optimization via Adversarial Influence Functions ICML 2020
(α-β) Zhun Deng, Cynthia Dwork, Jialiang Wang, Linjun Zhang
Towards Understanding the Dynamics of the First-Order Adversaries ICML 2020
Zhun Deng, Hangfeng He, Jiaoyang Huang, Weijie Su
2018
The Number of Independent Sets in Hexagonal Graphs ISIT 2018
Zhun Deng, Jie Ding, Kathryn Heal, Vahid Tarokh
Workshop Proceedings
2026
Who Should We Listen to More? Welfare-Aware Preference Acquisition for Pluralistic Alignment NeurIPS Workshop (FLLMPT) 2026
Ruomeng Ding*, Tianwei Gao*, Lianrui Geng, Mohit Bansal, Zhun Deng
2023
Last-layer Fairness Fine-tuning is Simple and Effective for Neural Networks ICML Workshop 2023
Yuzhen Mao, Zhun Deng, Huaxiu Yao, Ting Ye, Kenji Kawaguchi, James Zou
2019
Differential Privacy After the Fact: The Case of Congressional Reapportionment TPDP 2019
(α-β) Zhun Deng, Cynthia Dwork, Adam Smith
Journal Publications
To appear
Recommending Best Paper Awards for ML/AI Conferences via the Isotonic Mechanism JMLR, to appear
Garrett G. Wen, Buxin Su, Natalie Collina, Zhun Deng, Weijie Su
2025
Reliable and Responsible Foundation Models TMLR (Survey Certification) 2025
With Xinyu Yang et al.
2024
Improving Predictor Reliability with Selective Recalibration Transactions on Machine Learning Research 2024
Tom Zollo, Zhun Deng, Jake Snell, Toniann Pitassi, Richard Zemel
2023
The Power of Contrast for Feature Learning: A Theoretical Analysis The Journal of Machine Learning Research (JMLR), 24(330):1-78, 2023.
Wenlong Ji, Zhun Deng, Ryumei Nakada, James Zou, Linjun Zhang
2022
Understanding Dynamics of Learning Nonlinear Representations and its Application Neural Computation, 34(4), 991-1018, MIT Press, 2022.
Kenji Kawaguchi, Linjun Zhang, Zhun Deng