Universal outlying sequence detection for continuous observations. - Yuheng Bu; Shaofeng Zou; Yingbin Liang; Venugopal V. Veeravalli (ICASSP, 2016)

Estimation of KL divergence between large-alphabet distributions. - Yuheng Bu; Shaofeng Zou; Yingbin Liang; Venugopal V. Veeravalli (ISIT, 2016)

Linear Complexity Exponentially Consistent Tests for Outlying Sequence Detection. - Yuheng Bu; Shaofeng Zou; Venugopal V. Veeravalli (CoRR, 2017)

Active and Adaptive Sequential learning. - Yuheng Bu; Jiaxun Lu; Venugopal V. Veeravalli (CoRR, 2018)

Active Learning in Recommendation Systems with Multi-level User Preferences. - Yuheng Bu; Kevin Small (CoRR, 2018)

Estimation of KL Divergence: Optimal Minimax Rate. - Yuheng Bu; Shaofeng Zou; Yingbin Liang; Venugopal V. Veeravalli (IEEE Trans. Inf. Theory, 2018)

Active and Adaptive Sequential Learning with Per Time-step Excess Risk Guarantees. - Yuheng Bu; Jiaxun Lu; Venugopal V. Veeravalli (ACSSC, 2019)

Model Change Detection with Application to Machine Learning. - Yuheng Bu; Jiaxun Lu; Venugopal V. Veeravalli (ICASSP, 2019)

Adaptive Sequential Machine Learning. - Craig Wilson; Yuheng Bu; Venugopal V. Veeravalli (CoRR, 2019)

Linear-Complexity Exponentially-Consistent Tests for Universal Outlying Sequence Detection. - Yuheng Bu; Shaofeng Zou; Venugopal V. Veeravalli (IEEE Trans. Signal Process., 2019)

Information-theoretic bounds in learning algorithms - Yuheng Bu (2019)

Information-Theoretic Understanding of Population Risk Improvement with Model Compression. - Yuheng Bu; Weihao Gao; Shaofeng Zou; Venugopal V. Veeravalli (AAAI, 2020)

Tightening Mutual Information-Based Bounds on Generalization Error. - Yuheng Bu; Shaofeng Zou; Venugopal V. Veeravalli (IEEE J. Sel. Areas Inf. Theory, 2020)

Fair Selective Classification Via Sufficiency. - Joshua K. Lee; Yuheng Bu; Deepta Rajan; Prasanna Sattigeri; Rameswar Panda; Subhro Das; Gregory W. Wornell (ICML, 2021)

SDP Methods for Sensitivity-Constrained Privacy Funnel and Information Bottleneck Problems. - Yuheng Bu; Tony Wang; Gregory W. Wornell (ISIT, 2021)

An Exact Characterization of the Generalization Error for the Gibbs Algorithm. - Gholamali Aminian; Yuheng Bu; Laura Toni; Miguel R. D. Rodrigues; Gregory W. Wornell (NeurIPS, 2021)

Characterizing the Generalization Error of Gibbs Algorithm with Symmetrized KL information. - Gholamali Aminian; Yuheng Bu; Laura Toni; Miguel R. D. Rodrigues; Gregory W. Wornell (CoRR, 2021)

Population Risk Improvement with Model Compression: An Information-Theoretic Approach. - Yuheng Bu; Weihao Gao; Shaofeng Zou; Venugopal V. Veeravalli (Entropy, 2021)

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 (AISTATS, 2022)

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 (GLOBECOM, 2022)

A Maximal Correlation Approach to Imposing Fairness in Machine Learning. - Joshua K. Lee; Yuheng Bu; Prasanna Sattigeri; Rameswar Panda; Gregory W. Wornell; Leonid Karlinsky; Rogério Feris (ICASSP, 2022)

Selective Regression under Fairness Criteria. - Abhin Shah; Yuheng Bu; Joshua K. Lee; Subhro Das; Rameswar Panda; Prasanna Sattigeri; Gregory W. Wornell (ICML, 2022)

Tighter Expected Generalization Error Bounds via Convexity of Information Measures. - Gholamali Aminian; Yuheng Bu; Gregory W. Wornell; Miguel R. D. Rodrigues (ISIT, 2022)

Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation. - Gary C. F. Lee; Amir Weiss; Alejandro Lancho; Jennifer Tang; Yuheng Bu; Yury Polyanskiy; Gregory W. Wornell (MLSP, 2022)

On the Benefits of Selectivity in Pseudo-Labeling for Unsupervised Multi-Source-Free Domain Adaptation. - Maohao Shen; Yuheng Bu; Gregory W. Wornell (CoRR, 2022)

A Maximal Correlation Framework for Fair Machine Learning. - Joshua K. Lee; Yuheng Bu; Prasanna Sattigeri; Rameswar Panda; Gregory W. Wornell; Leonid Karlinsky; Rogério Schmidt Feris (Entropy, 2022)

Post-hoc Uncertainty Learning Using a Dirichlet Meta-Model. - Maohao Shen; Yuheng Bu; Prasanna Sattigeri; Soumya Ghosh; Subhro Das; Gregory W. Wornell (AAAI, 2023)

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 (AISTATS, 2023)

Reliable Gradient-free and Likelihood-free Prompt Tuning. - Maohao Shen; Soumya Ghosh; Prasanna Sattigeri; Subhro Das; Yuheng Bu; Gregory W. Wornell (EACL (Findings), 2023)

On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation. - Maohao Shen; Yuheng Bu; Gregory W. Wornell (ICML, 2023)

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 (ISIT, 2023)

A Bilateral Bound on the Mean-Square Error for Estimation in Model Mismatch. - Amir Weiss; Alejandro Lancho; Yuheng Bu; Gregory W. Wornell (ISIT, 2023)

Group Fairness with Uncertainty in Sensitive Attributes. - Abhin Shah; Maohao Shen; Jongha Jon Ryu; Subhro Das; Prasanna Sattigeri; Yuheng Bu; Gregory W. Wornell (CoRR, 2023)

Feature Learning in Image Hierarchies using Functional Maximal Correlation. - Bo Hu; Yuheng Bu; José C. Príncipe (CoRR, 2023)

SGLD-Based Information Criteria and the Over-Parameterized Regime. - Haobo Chen; Yuheng Bu; Gregory W. Wornell (CoRR, 2023)

Gibbs-Based Information Criteria and the Over-Parameterized Regime. - Haobo Chen; Gregory W. Wornell; Yuheng Bu (AISTATS, 2024)

An Algorithm for Computing the Capacity of Symmetrized KL Information for Discrete Channels. - Haobo Chen; Gholamali Aminian; Yuheng Bu (Allerton, 2024)

SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks. - Dingyi Zhuang; Yuheng Bu; Guang Wang 001; Shenhao Wang; Jinhua Zhao (SIGSPATIAL/GIS, 2024)

Learning Orthonormal Features in Self-Supervised Learning using Functional Maximal Correlation. - Bo Hu; Yuheng Bu; José C. Príncipe (ICIP, 2024)

Adaptive Text Watermark for Large Language Models. - Yepeng Liu; Yuheng Bu (ICML, 2024)

Operator SVD with Neural Networks via Nested Low-Rank Approximation. - Jongha Jon Ryu; Xiangxiang Xu 001; Hasan Sabri Melihcan Erol; Yuheng Bu; Lizhong Zheng; Gregory W. Wornell (ICML, 2024)

Information-Theoretic Opacity-Enforcement in Markov Decision Processes. - Chongyang Shi 002; Yuheng Bu; Jie Fu 002 (IJCAI, 2024)

Towards Optimal Inverse Temperature in the Gibbs Algorithm. - Yuheng Bu (ISIT, 2024)

Group Fairness with Uncertain Sensitive Attributes. - Abhin Shah; Maohao Shen; Jongha Jon Ryu; Subhro Das; Prasanna Sattigeri; Yuheng Bu; Gregory W. Wornell (ISIT, 2024)

Information-Theoretic Analysis of the Gibbs Algorithm: An Individual Sample Approach. - Youheng Zhu; Yuheng Bu (ITW, 2024)

Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage? - Maohao Shen; Jongha Jon Ryu; Soumya Ghosh; Yuheng Bu; Prasanna Sattigeri; Subhro Das; Gregory W. Wornell (NeurIPS, 2024)

Class-wise Generalization Error: an Information-Theoretic Analysis. - Firas Laakom; Yuheng Bu; Moncef Gabbouj (CoRR, 2024)

Improved Evidential Deep Learning via a Mixture of Dirichlet Distributions. - Jongha Jon Ryu; Maohao Shen; Soumya Ghosh; Yuheng Bu; Prasanna Sattigeri; Subhro Das; Gregory W. Wornell (CoRR, 2024)

Universally Optimal Watermarking Schemes for LLMs: from Theory to Practice. - Haiyun He; Yepeng Liu; Ziqiao Wang; Yongyi Mao; Yuheng Bu (CoRR, 2024)

Information-Theoretic Characterizations of Generalization Error for the Gibbs Algorithm. - Gholamali Aminian; Yuheng Bu; Laura Toni; Miguel R. D. Rodrigues; Gregory W. Wornell (IEEE Trans. Inf. Theory, 2024)

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 (ICLR, 2025)

Class-wise Generalization Error: an Information-Theoretic analysis. - Firas Laakom; Moncef Gabbouj; Jürgen Schmidhuber; Yuheng Bu (Trans. Mach. Learn. Res., 2025)

Distributional Information Embedding: A Framework for Multi-bit Watermarking. - Haiyun He; Yepeng Liu; Ziqiao Wang; Yongyi Mao; Yuheng Bu (CoRR, 2025)

Dataset Protection via Watermarked Canaries in Retrieval-Augmented LLMs. - Yepeng Liu; Xuandong Zhao; Dawn Song; Yuheng Bu (CoRR, 2025)

Defending LLM Watermarking Against Spoofing Attacks with Contrastive Representation Learning. - Li An; Yujian Liu; Yepeng Liu; Yang Zhang; Yuheng Bu; Shiyu Chang (CoRR, 2025)

In-Context Watermarks for Large Language Models. - Yepeng Liu; Xuandong Zhao; Christopher Kruegel; Dawn Song; Yuheng Bu (CoRR, 2025)

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective. - Firas Laakom; Haobo Chen; Jürgen Schmidhuber; Yuheng Bu (CoRR, 2025)

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting. - Yang Xing; Jiong Wu; Yuheng Bu; Kuang Gong (CoRR, 2025)

Graph neural networks for residential location choice: connection to classical logit models. - Zhanhong Cheng; Lingqian Hu; Yuheng Bu; Yuqi Zhou; Shenhao Wang (CoRR, 2025)

UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction. - Dahai Yu; Dingyi Zhuang; Lin Jiang 007; Rongchao Xu; Xinyue Ye; Yuheng Bu; Shenhao Wang; Guang Wang 001 (CoRR, 2025)