Publications
(* for equal contribution; (α-β) for alphabetical ordering; papers with purple titles are not on arXiv yet)
Conference Proceedings
2026
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
Adversarially Robust Control of Conditional Value-at-Risk via Kelly Conformal Inference ICML 2026
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
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
Chatherine Chen, Jingyan Shen, Zhun Deng, Lihua Lei
2024
Improving Predictor Reliability with Selective Recalibration Transactions on Machine Learning Research
Tom Zollo, Zhun Deng, Jake Snell, Toniann Pitassi, Richard Zemel
Learning and Forgetting Unsafe Examples in Large Language Models ICML 2024
Jiachen Zhao, Zhun Deng, David Madras, James Zou, Mengye Ren
Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models ICLR 2024
Tom Zollo, Todd Morrill*, Zhun Deng*, Jake Snell, Toniann Pitassi, Richard Zemel
2023
Distribution-free Statistical Dispersion Control for Societal Applications NeurIPS (spotlight, top 3% among submissions) 2023
Zhun Deng, Tom Zollo, Jake 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 Parkes
Understanding Multimodal Contrastive Learning and Incoportate Paired Data AISTATS 2023
Ryumei Nakada, Ibriham Golluk, 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 Snell, Tom 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
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
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
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
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