Zhun Deng

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Linjun ZhangAssistant Professor of Statistics, Rutgers UniversityVerified email at rutgers.edu
James ZouStanford UniversityVerified email at stanford.edu
Kenji KawaguchiPresidential Young Professor, National University of SingaporeVerified email at nus.edu.sg
Weijie SuAssociate Professor, University of PennsylvaniaVerified email at wharton.upenn.edu
Cynthia DworkMicrosoft ResearchVerified email at microsoft.com
Jiaoyang HuangUniversity of PennsylvaniaVerified email at wharton.upenn.edu
Huaxiu YaoAssistant Professor of Computer Science and Data Science, UNC Chapel HillVerified email at cs.unc.edu
Richard ZemelProfessor of Computer Science, University of TorontoVerified email at cs.toronto.edu
Jake C. SnellPostdoctoral Researcher, Princeton UniversityVerified email at princeton.edu
Chelsea FinnStanford University, Physical IntelligenceVerified email at cs.stanford.edu
Mohit BansalParker Distinguished Professor, Computer Science, UNC Chapel HillVerified email at cs.unc.edu
David C. ParkesGeorge F. Colony Professor of Computer Science, Harvard UniversityVerified email at eecs.harvard.edu
Ting YeUniversity of WashingtonVerified email at uw.edu
Zhiwei Steven WuCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Hui ChenProfessor of Finance, MITVerified email at mit.edu
David MadrasUniversity of TorontoVerified email at cs.toronto.edu
Mengye RenNYUVerified email at nyu.edu
R Yates ColeyKaiser Permanente Washington Health Research InstituteVerified email at kp.org
Dan RothProfessor of Computer Science, University of PennsylvaniaVerified email at seas.upenn.edu

Zhun Deng

Incoming Assistant Professor, CS@UNC Chapel Hill; Postdoc, Columbia University

Verified email at columbia.edu - Homepage

machine learning optimization statistics theoretical computer science


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
How does mixup help with robustness and generalization? L Zhang, Z Deng, K Kawaguchi, A Ghorbani, J Zou arXiv preprint arXiv:2010.04819, 2020	269	2020
Analyzing and mitigating object hallucination in large vision-language models Y Zhou, C Cui, J Yoon, L Zhang, Z Deng, C Finn, M Bansal, H Yao arXiv preprint arXiv:2310.00754, 2023	153	2023
An unconstrained layer-peeled perspective on neural collapse W Ji, Y Lu, Y Zhang, Z Deng, WJ Su arXiv preprint arXiv:2110.02796, 2021	80	2021
When and how mixup improves calibration L Zhang, Z Deng, K Kawaguchi, J Zou International Conference on Machine Learning, 26135-26160, 2022	65	2022
Adversarial training helps transfer learning via better representations Z Deng, L Zhang, K Vodrahalli, K Kawaguchi, JY Zou Advances in Neural Information Processing Systems 34, 25179-25191, 2021	55	2021
How does information bottleneck help deep learning? K Kawaguchi, Z Deng, X Ji, J Huang International Conference on Machine Learning, 16049-16096, 2023	52	2023
Toward better generalization bounds with locally elastic stability Z Deng, H He, W Su International Conference on Machine Learning, 2590-2600, 2021	40	2021
The power of contrast for feature learning: A theoretical analysis W Ji, Z Deng, R Nakada, J Zou, L Zhang Journal of Machine Learning Research 24 (330), 1-78, 2023	36	2023
Understanding multimodal contrastive learning and incorporating unpaired data R Nakada, HI Gulluk, Z Deng, W Ji, J Zou, L Zhang International Conference on Artificial Intelligence and Statistics, 4348-4380, 2023	29	2023
Improving adversarial robustness via unlabeled out-of-domain data Z Deng, L Zhang, A Ghorbani, J Zou International Conference on Artificial Intelligence and Statistics, 2845-2853, 2021	27	2021
Decision-aware conditional gans for time series data H Sun, Z Deng, H Chen, D Parkes Proceedings of the Fourth ACM International Conference on AI in Finance, 36-45, 2023	24	2023
Robustness implies generalization via data-dependent generalization bounds K Kawaguchi, Z Deng, K Luh, J Huang International conference on machine learning, 10866-10894, 2022	24	2022
Happymap: A generalized multi-calibration method Z Deng, C Dwork, L Zhang arXiv preprint arXiv:2303.04379, 2023	23	2023
Representation via representations: Domain generalization via adversarially learned invariant representations Z Deng, F Ding, C Dwork, R Hong, G Parmigiani, P Patil, P Sur arXiv preprint arXiv:2006.11478, 2020	23	2020
Learning and forgetting unsafe examples in large language models J Zhao, Z Deng, D Madras, J Zou, M Ren arXiv preprint arXiv:2312.12736, 2023	21	2023
Last-layer fairness fine-tuning is simple and effective for neural networks Y Mao, Z Deng, H Yao, T Ye, K Kawaguchi, J Zou arXiv preprint arXiv:2304.03935, 2023	17	2023
Fifa: Making fairness more generalizable in classifiers trained on imbalanced data Z Deng, J Zhang, L Zhang, T Ye, Y Coley, WJ Su, J Zou arXiv preprint arXiv:2206.02792, 2022	17	2022
Interpreting robust optimization via adversarial influence functions Z Deng, C Dwork, J Wang, L Zhang International Conference on Machine Learning, 2464-2473, 2020	16	2020
Scaffolding sets M Burhanpurkar, Z Deng, C Dwork, L Zhang arXiv preprint arXiv:2111.03135, 2021	14	2021
Reinforcement learning with stepwise fairness constraints Z Deng, H Sun, ZS Wu, L Zhang, DC Parkes arXiv preprint arXiv:2211.03994, 2022	13	2022

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