The number of independent sets in hexagonal graphs. - Zhun Deng; Jie Ding 002; Kathryn Heal; Vahid Tarokh (ISIT, 2017)

Interpreting Robust Optimization via Adversarial Influence Functions. - Zhun Deng; Cynthia Dwork; Jialiang Wang 001; Linjun Zhang (ICML, 2020)

Towards Understanding the Dynamics of the First-Order Adversaries. - Zhun Deng; Hangfeng He 001; Jiaoyang Huang; Weijie J. Su (ICML, 2020)

Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations. - Zhun Deng; Frances Ding; Cynthia Dwork; Rachel Hong; Giovanni Parmigiani; Prasad Patil; Pragya Sur (CoRR, 2020)

Improving Adversarial Robustness via Unlabeled Out-of-Domain Data. - Zhun Deng; Linjun Zhang; Amirata Ghorbani; James Zou 001 (AISTATS, 2021)

How Shrinking Gradient Noise Helps the Performance of Neural Networks. - Zhun Deng; Jiaoyang Huang; Kenji Kawaguchi (IEEE BigData, 2021)

How Does Mixup Help With Robustness and Generalization? - Linjun Zhang; Zhun Deng; Kenji Kawaguchi; Amirata Ghorbani; James Zou 001 (ICLR, 2021)

Toward Better Generalization Bounds with Locally Elastic Stability. - Zhun Deng; Hangfeng He 001; Weijie J. Su (ICML, 2021)

Adversarial Training Helps Transfer Learning via Better Representations. - Zhun Deng; Linjun Zhang; Kailas Vodrahalli; Kenji Kawaguchi; James Y. Zou (NeurIPS, 2021)

Scaffolding Sets. - Maya Burhanpurkar; Zhun Deng; Cynthia Dwork; Linjun Zhang (CoRR, 2021)

An Unconstrained Layer-Peeled Perspective on Neural Collapse. - Wenlong Ji; Yiping Lu 001; Yiliang Zhang; Zhun Deng; Weijie J. Su (ICLR, 2022)

Robustness Implies Generalization via Data-Dependent Generalization Bounds. - Kenji Kawaguchi; Zhun Deng; Kyle Luh; Jiaoyang Huang (ICML, 2022)

When and How Mixup Improves Calibration. - Linjun Zhang; Zhun Deng; Kenji Kawaguchi; James Zou 001 (ICML, 2022)

Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach. - Jiayao Zhang 001; Hongming Zhang 009; Zhun Deng; Dan Roth 001 (CoRR, 2022)

Understanding Dynamics of Nonlinear Representation Learning and Its Application. - Kenji Kawaguchi; Linjun Zhang; Zhun Deng (Neural Comput., 2022)

Reinforcement Learning with Stepwise Fairness Constraints. - Zhun Deng; He Sun; Steven Wu 001; Linjun Zhang; David C. Parkes (AISTATS, 2023)

Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data. - Ryumei Nakada; Halil Ibrahim Gulluk; Zhun Deng; Wenlong Ji; James Zou 001; Linjun Zhang (AISTATS, 2023)

Decision-Aware Conditional GANs for Time Series Data. - He Sun; Zhun Deng; Hui Chen; David C. Parkes (ICAIF, 2023)

FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data. - Zhun Deng; Jiayao Zhang 001; Linjun Zhang; Ting Ye; Yates Coley; Weijie J. Su; James Zou 001 (ICLR, 2023)

Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions. - Jake Snell; Thomas P. Zollo; Zhun Deng; Toniann Pitassi; Richard S. Zemel (ICLR, 2023)

How Does Information Bottleneck Help Deep Learning? - Kenji Kawaguchi; Zhun Deng; Xu Ji; Jiaoyang Huang (ICML, 2023)

HappyMap : A Generalized Multicalibration Method. - Zhun Deng; Cynthia Dwork; Linjun Zhang (ITCS, 2023)

Distribution-Free Statistical Dispersion Control for Societal Applications. - Zhun Deng; Thomas P. Zollo; Jake Snell; Toniann Pitassi; Richard S. Zemel (NeurIPS, 2023)

PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification. - Qianli Shen; Wai Hoh Tang; Zhun Deng; Apostolos F. Psaros; Kenji Kawaguchi (NeurIPS, 2023)

HappyMap: A Generalized Multi-calibration Method. - Zhun Deng; Cynthia Dwork; Linjun Zhang (CoRR, 2023)

Last-Layer Fairness Fine-tuning is Simple and Effective for Neural Networks. - Yuzhen Mao; Zhun Deng; Huaxiu Yao; Ting Ye; Kenji Kawaguchi; James Zou 001 (CoRR, 2023)

The Power of Contrast for Feature Learning: A Theoretical Analysis. - Wenlong Ji; Zhun Deng; Ryumei Nakada; James Zou 001; Linjun Zhang (J. Mach. Learn. Res., 2023)

Analyzing and Mitigating Object Hallucination in Large Vision-Language Models. - Yiyang Zhou; Chenhang Cui; Jaehong Yoon; Linjun Zhang; Zhun Deng; Chelsea Finn; Mohit Bansal; Huaxiu Yao (ICLR, 2024)

Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models. - Thomas P. Zollo; Todd Morrill; Zhun Deng; Jake Snell; Toniann Pitassi; Richard S. Zemel (ICLR, 2024)

Learning and Forgetting Unsafe Examples in Large Language Models. - Jiachen Zhao; Zhun Deng; David Madras; James Zou 001; Mengye Ren (ICML, 2024)

Can AI Be as Creative as Humans? - Haonan Wang; James Zou 001; Michael Mozer; Anirudh Goyal; Alex Lamb; Linjun Zhang; Weijie J. Su; Zhun Deng; Michael Qizhe Xie; Hannah Brown; Kenji Kawaguchi (CoRR, 2024)

Provable Multi-Party Reinforcement Learning with Diverse Human Feedback. - Huiying Zhong; Zhun Deng; Weijie J. Su; Zhiwei Steven Wu; Linjun Zhang (CoRR, 2024)

An Economic Solution to Copyright Challenges of Generative AI. - Jiachen T. Wang; Zhun Deng; Hiroaki Chiba-Okabe; Boaz Barak; Weijie J. Su (CoRR, 2024)

Improving Predictor Reliability with Selective Recalibration. - Thomas P. Zollo; Zhun Deng; Jake Snell; Toniann Pitassi; Richard S. Zemel (Trans. Mach. Learn. Res., 2024)

Synergistic Weak-Strong Collaboration by Aligning Preferences. - Yizhu Jiao; Xuchao Zhang; Zhaoyang Wang 004; Yubo Ma; Zhun Deng; Rujia Wang; Chetan Bansal; Saravan Rajmohan; Jiawei Han 001; Huaxiu Yao (ACL (1), 2025)

Conformal Tail Risk Control for Large Language Model Alignment. - Catherine Yu-Chi Chen; Jingyan Shen; Zhun Deng; Lihua Lei (CoRR, 2025)

Statistical Inference under Performativity. - Xiang Li; Yunai Li; Huiying Zhong; Lihua Lei; Zhun Deng (CoRR, 2025)

Performative Risk Control: Calibrating Models for Reliable Deployment under Performativity. - Victor Li; Baiting Chen; Yuzhen Mao; Qi Lei; Zhun Deng (CoRR, 2025)

QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions. - Zhun Deng; Thomas P. Zollo; Benjamin Eyre; Amogh Inamdar; David Madras; Richard S. Zemel (CoRR, 2025)