Research Interests

I am broadly interested in problems at the frontier of machine learning, learning theory, and computational social science. Here are some recent topics I am interested in:

• Uncertainty quantification (conformal prediction, calibration)

• Risk control, especially for foundation models (e.g., LLMs) and robotics

• Algorithmic fairness

• Sequential decision-making in social science/social dynamics

Publications

(* for equal contribution; (α-β) for alphabetical ordering)

Conference Proceedings

2024

Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models ICLR 2024

Tom Zello, 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 Zello, 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 Zello, 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

Talks

• Lunch Seminar, Center for Data Science, NYU, "Taming the Beast: Practical Theories for Responsible Machine Learning".

• Theory Lunch Seminar, CMU, "Taming the Beast: Practical Theories for Responsible Machine Learning".

• Penn Research in Machine Learning forum, University of Pennsylvania, "Knowing the Unknowns: Uncertainty Quantification for Responsible AI Deployment".

• ITCS 2023, "Happymap: a generalized multi-calibration method".

• AI TIME, one hour talk, 2022 "Reinforcement Learning with Stepwise Fairness Constraints".

• Microsoft Research Asia, Beijing, 2022, "New Tools in Algoritmic Statbility".

• JSM 2022,  "Obtaining More Generalizable Fair Classifiers on Imbalanced Datasets".

• ICML 2022, long talk "Robustness Implies Generalization via Data-dependent Generalization Bounds".

• Simons Institute for the Theory of Computing, Data Privacy: Foundations and Applications Reunion, 2022 "Scaffolding Sets".

• Tsinghua University, AI TIME, 2022 "Adversarial Training Encourages More Transferable Representations".

• NeurIPS 2021, "Adversarial Training Helps Transfer Learning via Better Representations".

• ICML 2021, "Towards Better Generalization Bounds with Locally Elastic Stability".

• ICLR 2021, "How Does Mixup Helps Robustness and Generalization".

• AISTATS 2021, "Improving Adversarial Robustness via Unalabeled Out-of-Domain Data".

• ICML 2020, "The Optimization Landscape of the First-Order Adversaries".

• University of Minnesota Twin Cities, Jie Ding's Group, 2018 "Recent Advances in Differential Privacy: Theory And Application".