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2020 – today
- 2024
[c23]Yiyang Zhou, Chenhang Cui, Jaehong Yoon, Linjun Zhang, Zhun Deng, Chelsea Finn, Mohit Bansal, Huaxiu Yao:
Analyzing and Mitigating Object Hallucination in Large Vision-Language Models. ICLR 2024- [c22]Thomas P. Zollo, Todd Morrill, Zhun Deng, Jake Snell, Toniann Pitassi, Richard S. Zemel:
Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models. ICLR 2024 - [c21]Jiachen Zhao, Zhun Deng, David Madras, James Zou, Mengye Ren:
Learning and Forgetting Unsafe Examples in Large Language Models. ICML 2024 - [i31]Haonan Wang, James Zou, Michael Mozer, Anirudh Goyal, Alex Lamb, Linjun Zhang, Weijie J. Su, Zhun Deng, Michael Qizhe Xie, Hannah Brown, Kenji Kawaguchi:
Can AI Be as Creative as Humans? CoRR abs/2401.01623 (2024) - [i30]Huiying Zhong, Zhun Deng, Weijie J. Su, Zhiwei Steven Wu, Linjun Zhang:
Provable Multi-Party Reinforcement Learning with Diverse Human Feedback. CoRR abs/2403.05006 (2024) - [i29]Jiachen T. Wang, Zhun Deng, Hiroaki Chiba-Okabe, Boaz Barak, Weijie J. Su:
An Economic Solution to Copyright Challenges of Generative AI. CoRR abs/2404.13964 (2024) - 2023
- [j2]Wenlong Ji, Zhun Deng, Ryumei Nakada, James Zou, Linjun Zhang:
The Power of Contrast for Feature Learning: A Theoretical Analysis. J. Mach. Learn. Res. 24: 330:1-330:78 (2023) - [c20]Ryumei Nakada, Halil Ibrahim Gulluk, Zhun Deng, Wenlong Ji, James Zou, Linjun Zhang:
Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data. AISTATS 2023: 4348-4380 - [c19]Zhun Deng, He Sun, Steven Wu, Linjun Zhang, David C. Parkes:
Reinforcement Learning with Stepwise Fairness Constraints. AISTATS 2023: 10594-10618 - [c18]He Sun
, Zhun Deng, Hui Chen, David C. Parkes:
Decision-Aware Conditional GANs for Time Series Data. ICAIF 2023: 36-45 - [c17]Zhun Deng, Jiayao Zhang, Linjun Zhang, Ting Ye, Yates Coley, Weijie J. Su, James Zou:
FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data. ICLR 2023 - [c16]Jake Snell, Thomas P. Zollo, Zhun Deng, Toniann Pitassi, Richard S. Zemel:
Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions. ICLR 2023 - [c15]Kenji Kawaguchi, Zhun Deng, Xu Ji, Jiaoyang Huang:
How Does Information Bottleneck Help Deep Learning? ICML 2023: 16049-16096 - [c14]Zhun Deng, Cynthia Dwork, Linjun Zhang:
HappyMap : A Generalized Multicalibration Method. ITCS 2023: 41:1-41:23 - [c13]Zhun Deng, Thomas P. Zollo, Jake Snell, Toniann Pitassi, Richard S. Zemel:
Distribution-Free Statistical Dispersion Control for Societal Applications. NeurIPS 2023 - [c12]Qianli Shen, Wai Hoh Tang, Zhun Deng, Apostolos F. Psaros, Kenji Kawaguchi:
PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification. NeurIPS 2023 - [i28]Ryumei Nakada, Halil Ibrahim Gulluk, Zhun Deng, Wenlong Ji, James Zou, Linjun Zhang:
Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data. CoRR abs/2302.06232 (2023) - [i27]Zhun Deng, Cynthia Dwork, Linjun Zhang:
HappyMap: A Generalized Multi-calibration Method. CoRR abs/2303.04379 (2023) - [i26]Yuzhen Mao, Zhun Deng, Huaxiu Yao, Ting Ye, Kenji Kawaguchi, James Zou:
Last-Layer Fairness Fine-tuning is Simple and Effective for Neural Networks. CoRR abs/2304.03935 (2023) - [i25]Kenji Kawaguchi, Zhun Deng, Xu Ji, Jiaoyang Huang:
How Does Information Bottleneck Help Deep Learning? CoRR abs/2305.18887 (2023) - [i24]Zhun Deng, Thomas P. Zollo, Jake C. Snell, Toniann Pitassi, Richard S. Zemel:
Distribution-Free Statistical Dispersion Control for Societal Applications. CoRR abs/2309.13786 (2023) - [i23]Yiyang Zhou, Chenhang Cui, Jaehong Yoon, Linjun Zhang, Zhun Deng, Chelsea Finn, Mohit Bansal, Huaxiu Yao:
Analyzing and Mitigating Object Hallucination in Large Vision-Language Models. CoRR abs/2310.00754 (2023) - [i22]Qianli Shen, Wai Hoh Tang, Zhun Deng, Apostolos F. Psaros, Kenji Kawaguchi:
PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification. CoRR abs/2310.06923 (2023) - [i21]Thomas P. Zollo, Todd Morrill, Zhun Deng, Jake C. Snell, Toniann Pitassi, Richard S. Zemel:
Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models. CoRR abs/2311.13628 (2023) - [i20]Jiachen Zhao, Zhun Deng, David Madras, James Zou, Mengye Ren:
Learning and Forgetting Unsafe Examples in Large Language Models. CoRR abs/2312.12736 (2023) - 2022
- [j1]Kenji Kawaguchi, Linjun Zhang, Zhun Deng:
Understanding Dynamics of Nonlinear Representation Learning and Its Application. Neural Comput. 34(4): 991-1018 (2022) - [c11]Wenlong Ji, Yiping Lu, Yiliang Zhang, Zhun Deng, Weijie J. Su:
An Unconstrained Layer-Peeled Perspective on Neural Collapse. ICLR 2022 - [c10]Kenji Kawaguchi, Zhun Deng, Kyle Luh, Jiaoyang Huang:
Robustness Implies Generalization via Data-Dependent Generalization Bounds. ICML 2022: 10866-10894 - [c9]Linjun Zhang, Zhun Deng, Kenji Kawaguchi, James Zou:
When and How Mixup Improves Calibration. ICML 2022: 26135-26160 - [i19]Zhun Deng, Jiayao Zhang, Linjun Zhang, Ting Ye, Yates Coley, Weijie J. Su, James Zou:
FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data. CoRR abs/2206.02792 (2022) - [i18]Kenji Kawaguchi, Zhun Deng, Kyle Luh, Jiaoyang Huang:
Robustness Implies Generalization via Data-Dependent Generalization Bounds. CoRR abs/2206.13497 (2022) - [i17]Zhun Deng, He Sun, Zhiwei Steven Wu, Linjun Zhang, David C. Parkes:
Reinforcement Learning with Stepwise Fairness Constraints. CoRR abs/2211.03994 (2022) - [i16]Jiayao Zhang, Hongming Zhang, Zhun Deng, Dan Roth:
Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach. CoRR abs/2211.06398 (2022) - [i15]Jake C. Snell, Thomas P. Zollo, Zhun Deng, Toniann Pitassi, Richard S. Zemel:
Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions. CoRR abs/2212.13629 (2022) - 2021
- [c8]Zhun Deng, Linjun Zhang, Amirata Ghorbani, James Zou:
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data. AISTATS 2021: 2845-2853 - [c7]Zhun Deng, Jiaoyang Huang, Kenji Kawaguchi:
How Shrinking Gradient Noise Helps the Performance of Neural Networks. IEEE BigData 2021: 1002-1007 - [c6]Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Zou:
How Does Mixup Help With Robustness and Generalization? ICLR 2021 - [c5]Zhun Deng, Hangfeng He, Weijie J. Su:
Toward Better Generalization Bounds with Locally Elastic Stability. ICML 2021: 2590-2600 - [c4]Zhun Deng, Linjun Zhang, Kailas Vodrahalli, Kenji Kawaguchi, James Y. Zou:
Adversarial Training Helps Transfer Learning via Better Representations. NeurIPS 2021: 25179-25191 - [i14]Linjun Zhang, Zhun Deng, Kenji Kawaguchi, James Zou:
When and How Mixup Improves Calibration. CoRR abs/2102.06289 (2021) - [i13]Zhun Deng, Linjun Zhang, Kailas Vodrahalli, Kenji Kawaguchi, James Zou:
Adversarial Training Helps Transfer Learning via Better Representations. CoRR abs/2106.10189 (2021) - [i12]Kenji Kawaguchi, Linjun Zhang, Zhun Deng:
Understanding Dynamics of Nonlinear Representation Learning and Its Application. CoRR abs/2106.14836 (2021) - [i11]Wenlong Ji, Zhun Deng, Ryumei Nakada, James Zou, Linjun Zhang:
The Power of Contrast for Feature Learning: A Theoretical Analysis. CoRR abs/2110.02473 (2021) - [i10]Wenlong Ji, Yiping Lu, Yiliang Zhang, Zhun Deng, Weijie J. Su:
An Unconstrained Layer-Peeled Perspective on Neural Collapse. CoRR abs/2110.02796 (2021) - [i9]Maya Burhanpurkar, Zhun Deng, Cynthia Dwork, Linjun Zhang:
Scaffolding Sets. CoRR abs/2111.03135 (2021) - 2020
- [c3]Zhun Deng, Cynthia Dwork, Jialiang Wang, Linjun Zhang:
Interpreting Robust Optimization via Adversarial Influence Functions. ICML 2020: 2464-2473 - [c2]Zhun Deng, Hangfeng He, Jiaoyang Huang, Weijie J. Su:
Towards Understanding the Dynamics of the First-Order Adversaries. ICML 2020: 2484-2493 - [i8]Zhun Deng, Linjun Zhang, Amirata Ghorbani, James Y. Zou:
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data. CoRR abs/2006.08476 (2020) - [i7]Zhun Deng, Frances Ding, Cynthia Dwork, Rachel Hong, Giovanni Parmigiani, Prasad Patil, Pragya Sur:
Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations. CoRR abs/2006.11478 (2020) - [i6]He Sun, Zhun Deng, Hui Chen, David C. Parkes:
Decision-Aware Conditional GANs for Time Series Data. CoRR abs/2009.12682 (2020) - [i5]Zhun Deng, Cynthia Dwork, Jialiang Wang, Linjun Zhang:
Interpreting Robust Optimization via Adversarial Influence Functions. CoRR abs/2010.01247 (2020) - [i4]Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Y. Zou:
How Does Mixup Help With Robustness and Generalization? CoRR abs/2010.04819 (2020) - [i3]Zhun Deng, Hangfeng He, Jiaoyang Huang, Weijie J. Su:
Towards Understanding the Dynamics of the First-Order Adversaries. CoRR abs/2010.10650 (2020) - [i2]Zhun Deng, Hangfeng He, Weijie J. Su:
Toward Better Generalization Bounds with Locally Elastic Stability. CoRR abs/2010.13988 (2020)
2010 – 2019
- 2019
- [i1]Zhun Deng, Cynthia Dwork, Jialiang Wang, Yao Zhao:
Architecture Selection via the Trade-off Between Accuracy and Robustness. CoRR abs/1906.01354 (2019) - 2017
- [c1]Zhun Deng, Jie Ding, Kathryn Heal, Vahid Tarokh:
The number of independent sets in hexagonal graphs. ISIT 2017: 2910-2914
Coauthor Index
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last updated on 2024-09-14 01:12 CEST by the dblp team
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