Zhun Deng

Orcid: 0009-0002-3785-8513

According to our database1, Zhun Deng authored at least 35 papers between 2017 and 2024.

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Bibliography

2024
Improving Predictor Reliability with Selective Recalibration.
CoRR, 2024

An Economic Solution to Copyright Challenges of Generative AI.
CoRR, 2024

Provable Multi-Party Reinforcement Learning with Diverse Human Feedback.
CoRR, 2024

Can AI Be as Creative as Humans?
CoRR, 2024

Learning and Forgetting Unsafe Examples in Large Language Models.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Analyzing and Mitigating Object Hallucination in Large Vision-Language Models.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
The Power of Contrast for Feature Learning: A Theoretical Analysis.
J. Mach. Learn. Res., 2023

Last-Layer Fairness Fine-tuning is Simple and Effective for Neural Networks.
CoRR, 2023

HappyMap: A Generalized Multi-calibration Method.
CoRR, 2023

PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Distribution-Free Statistical Dispersion Control for Societal Applications.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

HappyMap : A Generalized Multicalibration Method.
Proceedings of the 14th Innovations in Theoretical Computer Science Conference, 2023

How Does Information Bottleneck Help Deep Learning?
Proceedings of the International Conference on Machine Learning, 2023

Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Decision-Aware Conditional GANs for Time Series Data.
Proceedings of the 4th ACM International Conference on AI in Finance, 2023

Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Reinforcement Learning with Stepwise Fairness Constraints.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Understanding Dynamics of Nonlinear Representation Learning and Its Application.
Neural Comput., 2022

Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach.
CoRR, 2022

When and How Mixup Improves Calibration.
Proceedings of the International Conference on Machine Learning, 2022

Robustness Implies Generalization via Data-Dependent Generalization Bounds.
Proceedings of the International Conference on Machine Learning, 2022

An Unconstrained Layer-Peeled Perspective on Neural Collapse.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
Scaffolding Sets.
CoRR, 2021

Adversarial Training Helps Transfer Learning via Better Representations.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Toward Better Generalization Bounds with Locally Elastic Stability.
Proceedings of the 38th International Conference on Machine Learning, 2021

How Does Mixup Help With Robustness and Generalization?
Proceedings of the 9th International Conference on Learning Representations, 2021

How Shrinking Gradient Noise Helps the Performance of Neural Networks.
Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

Improving Adversarial Robustness via Unlabeled Out-of-Domain Data.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations.
CoRR, 2020

Towards Understanding the Dynamics of the First-Order Adversaries.
Proceedings of the 37th International Conference on Machine Learning, 2020

Interpreting Robust Optimization via Adversarial Influence Functions.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Architecture Selection via the Trade-off Between Accuracy and Robustness.
CoRR, 2019

2017
The number of independent sets in hexagonal graphs.
Proceedings of the 2017 IEEE International Symposium on Information Theory, 2017


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