| 2026 | AAAI | TrustEnergy: A Unified Framework for Accurate and Reliable User-level Energy Usage Prediction. | Dahai Yu, Rongchao Xu, Dingyi Zhuang, Yuheng Bu, Shenhao Wang, Guang Wang |
| 2026 | ACL | Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption. | Yepeng Liu, Xuandong Zhao, Dawn Song, Gregory W. Wornell, Yuheng Bu |
| 2026 | EACL | A Reinforcement Learning Framework for Robust and Secure LLM Watermarking. | Li An, Yujian Liu, Yepeng Liu, Yuheng Bu, Yang Zhang, Shiyu Chang |
| 2025 | ICLR | Image Watermarks are Removable using Controllable Regeneration from Clean Noise. | Yepeng Liu, Yiren Song, Hai Ci, Yu Zhang, Haofan Wang, Mike Zheng Shou, Yuheng Bu |
| 2025 | ICML | Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective. | Firas Laakom, Haobo Chen, Jrgen Schmidhuber, Yuheng Bu |
| 2024 | AISTATS | Gibbs-Based Information Criteria and the Over-Parameterized Regime. | Haobo Chen, Gregory W. Wornell, Yuheng Bu |
| 2024 | ICIP | Learning Orthonormal Features in Self-Supervised Learning using Functional Maximal Correlation. | Bo Hu, Yuheng Bu, Jos C. Prncipe |
| 2024 | ICML | Adaptive Text Watermark for Large Language Models. | Yepeng Liu, Yuheng Bu |
| 2024 | ICML | Operator SVD with Neural Networks via Nested Low-Rank Approximation. | Jongha Jon Ryu, Xiangxiang Xu, Hasan Sabri Melihcan Erol, Yuheng Bu, Lizhong Zheng, Gregory W. Wornell |
| 2024 | IJCAI | Information-Theoretic Opacity-Enforcement in Markov Decision Processes. | Chongyang Shi, Yuheng Bu, Jie Fu |
| 2024 | ISIT | Towards Optimal Inverse Temperature in the Gibbs Algorithm. | Yuheng Bu |
| 2024 | ISIT | Group Fairness with Uncertain Sensitive Attributes. | Abhin Shah, Maohao Shen, Jongha Jon Ryu, Subhro Das, Prasanna Sattigeri, Yuheng Bu, Gregory W. Wornell |
| 2024 | ITW | Information-Theoretic Analysis of the Gibbs Algorithm: An Individual Sample Approach. | Youheng Zhu, Yuheng Bu |
| 2023 | AAAI | Post-hoc Uncertainty Learning Using a Dirichlet Meta-Model. | Maohao Shen, Yuheng Bu, Prasanna Sattigeri, Soumya Ghosh, Subhro Das, Gregory W. Wornell |
| 2023 | AISTATS | How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm? | Haiyun He, Gholamali Aminian, Yuheng Bu, Miguel R. D. Rodrigues, Vincent Y. F. Tan |
| 2023 | EACL | Reliable Gradient-free and Likelihood-free Prompt Tuning. | Maohao Shen, Soumya Ghosh, Prasanna Sattigeri, Subhro Das, Yuheng Bu, Gregory W. Wornell |
| 2023 | ICML | On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation. | Maohao Shen, Yuheng Bu, Gregory W. Wornell |
| 2023 | ISIT | On the Generalization Error of Meta Learning for the Gibbs Algorithm. | Yuheng Bu, Harsha Vardhan Tetali, Gholamali Aminian, Miguel R. D. Rodrigues, Gregory W. Wornell |
| 2023 | ISIT | A Bilateral Bound on the Mean-Square Error for Estimation in Model Mismatch. | Amir Weiss, Alejandro Lancho, Yuheng Bu, Gregory W. Wornell |
| 2022 | AISTATS | Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm. | Yuheng Bu, Gholamali Aminian, Laura Toni, Gregory W. Wornell, Miguel R. D. Rodrigues |
| 2022 | GLOBECOM | Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals. | Alejandro Lancho, Amir Weiss, Gary C. F. Lee, Jennifer Tang, Yuheng Bu, Yury Polyanskiy, Gregory W. Wornell |
| 2022 | ICASSP | A Maximal Correlation Approach to Imposing Fairness in Machine Learning. | Joshua K. Lee, Yuheng Bu, Prasanna Sattigeri, Rameswar Panda, Gregory W. Wornell, Leonid Karlinsky, Rogrio Feris |
| 2022 | ICML | Selective Regression under Fairness Criteria. | Abhin Shah, Yuheng Bu, Joshua K. Lee, Subhro Das, Rameswar Panda, Prasanna Sattigeri, Gregory W. Wornell |
| 2022 | ISIT | Tighter Expected Generalization Error Bounds via Convexity of Information Measures. | Gholamali Aminian, Yuheng Bu, Gregory W. Wornell, Miguel R. D. Rodrigues |
| 2021 | ICML | Fair Selective Classification Via Sufficiency. | Joshua K. Lee, Yuheng Bu, Deepta Rajan, Prasanna Sattigeri, Rameswar Panda, Subhro Das, Gregory W. Wornell |
| 2021 | ISIT | SDP Methods for Sensitivity-Constrained Privacy Funnel and Information Bottleneck Problems. | Yuheng Bu, Tony Wang, Gregory W. Wornell |
| 2020 | AAAI | Information-Theoretic Understanding of Population Risk Improvement with Model Compression. | Yuheng Bu, Weihao Gao, Shaofeng Zou, Venugopal V. Veeravalli |
| 2019 | ACSSC | Active and Adaptive Sequential Learning with Per Time-step Excess Risk Guarantees. | Yuheng Bu, Jiaxun Lu, Venugopal V. Veeravalli |
| 2019 | ICASSP | Model Change Detection with Application to Machine Learning. | Yuheng Bu, Jiaxun Lu, Venugopal V. Veeravalli |
| 2019 | ISIT | Tightening Mutual Information Based Bounds on Generalization Error. | Yuheng Bu, Shaofeng Zou, Venugopal V. Veeravalli |
| 2017 | ISIT | Linear-complexity exponentially-consistent tests for universal outlying sequence detection. | Yuheng Bu, Shaofeng Zou, Venugopal V. Veeravalli |
| 2016 | ICASSP | Universal outlying sequence detection for continuous observations. | Yuheng Bu, Shaofeng Zou, Yingbin Liang, Venugopal V. Veeravalli |
| 2016 | ISIT | Estimation of KL divergence between large-alphabet distributions. | Yuheng Bu, Shaofeng Zou, Yingbin Liang, Venugopal V. Veeravalli |