2025
Synergistic Weak-Strong Collaboration by Aligning Preferences.
CoRR, April, 2025
Conformal Tail Risk Control for Large Language Model Alignment.
CoRR, February, 2025
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?
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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
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