Yuheng Bu

According to our database, Yuheng Bu authored at least 49 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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, January, 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

CoRR, 2024

Universally Optimal Watermarking Schemes for LLMs: from Theory to Practice.

Haiyun He, Yepeng Liu, Ziqiao Wang, Yongyi Mao, Yuheng Bu

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

Class-wise Generalization Error: an Information-Theoretic Analysis.

Firas Laakom, Yuheng Bu, Moncef Gabbouj

CoRR, 2024

Information-Theoretic Analysis of the Gibbs Algorithm: An Individual Sample Approach.

Youheng Zhu, Yuheng Bu

Proceedings of the IEEE Information Theory Workshop, 2024

Group Fairness with Uncertain Sensitive Attributes.

Abhin Shah, Maohao Shen, Jongha Jon Ryu, Subhro Das, Prasanna Sattigeri, Yuheng Bu, Gregory W. Wornell

Proceedings of the IEEE International Symposium on Information Theory, 2024

Towards Optimal Inverse Temperature in the Gibbs Algorithm.

Yuheng Bu

Proceedings of the IEEE International Symposium on Information Theory, 2024

Information-Theoretic Opacity-Enforcement in Markov Decision Processes.

Chongyang Shi, Yuheng Bu, Jie Fu

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Adaptive Text Watermark for Large Language Models.

Yepeng Liu, Yuheng Bu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks.

Dingyi Zhuang, Yuheng Bu, Guang Wang, Shenhao Wang, Jinhua Zhao

Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems, 2024

An Algorithm for Computing the Capacity of Symmetrized KL Information for Discrete Channels.

Haobo Chen, Gholamali Aminian, Yuheng Bu

Proceedings of the 60th Annual Allerton Conference on Communication, 2024

Gibbs-Based Information Criteria and the Over-Parameterized Regime.

Haobo Chen, Gregory W. Wornell, Yuheng Bu

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

SGLD-Based Information Criteria and the Over-Parameterized Regime.

Haobo Chen, 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

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

A Bilateral Bound on the Mean-Square Error for Estimation in Model Mismatch.

Amir Weiss, Alejandro Lancho, Yuheng Bu, Gregory W. Wornell

Proceedings of the IEEE International Symposium on Information Theory, 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

Proceedings of the IEEE International Symposium on Information Theory, 2023

On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation.

Maohao Shen, Yuheng Bu, Gregory W. Wornell

Proceedings of the International Conference on Machine Learning, 2023

Reliable Gradient-free and Likelihood-free Prompt Tuning.

Maohao Shen, Soumya Ghosh, Prasanna Sattigeri, Subhro Das, Yuheng Bu, Gregory W. Wornell

Proceedings of the Findings of the Association for Computational Linguistics: EACL 2023, 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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Post-hoc Uncertainty Learning Using a Dirichlet Meta-Model.

Maohao Shen, Yuheng Bu, Prasanna Sattigeri, Soumya Ghosh, Subhro Das, Gregory W. Wornell

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

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

On the Benefits of Selectivity in Pseudo-Labeling for Unsupervised Multi-Source-Free Domain Adaptation.

Maohao Shen, Yuheng Bu, Gregory W. Wornell

CoRR, 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

Proceedings of the 32nd IEEE International Workshop on Machine Learning for Signal Processing, 2022

Tighter Expected Generalization Error Bounds via Convexity of Information Measures.

Gholamali Aminian, Yuheng Bu, Gregory W. Wornell, Miguel R. D. Rodrigues

Proceedings of the IEEE International Symposium on Information Theory, 2022

Selective Regression under Fairness Criteria.

Abhin Shah, Yuheng Bu, Joshua K. Lee, Subhro Das, Rameswar Panda, Prasanna Sattigeri, Gregory W. Wornell

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the IEEE International Conference on Acoustics, 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

Proceedings of the IEEE Global Communications Conference, 2022

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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Population Risk Improvement with Model Compression: An Information-Theoretic Approach.

Yuheng Bu, Weihao Gao, Shaofeng Zou, Venugopal V. Veeravalli

Entropy, 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

An Exact Characterization of the Generalization Error for the Gibbs Algorithm.

Gholamali Aminian, Yuheng Bu, Laura Toni, Miguel R. D. Rodrigues, Gregory W. Wornell

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

SDP Methods for Sensitivity-Constrained Privacy Funnel and Information Bottleneck Problems.

Yuheng Bu, Tony Wang, Gregory W. Wornell

Proceedings of the IEEE International Symposium on Information Theory, 2021

Fair Selective Classification Via Sufficiency.

Joshua K. Lee, Yuheng Bu, Deepta Rajan, Prasanna Sattigeri, Rameswar Panda, Subhro Das, Gregory W. Wornell

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Tightening Mutual Information-Based Bounds on Generalization Error.

Yuheng Bu, Shaofeng Zou, Venugopal V. Veeravalli

IEEE J. Sel. Areas Inf. Theory, 2020

Information-Theoretic Understanding of Population Risk Improvement with Model Compression.

Yuheng Bu, Weihao Gao, Shaofeng Zou, Venugopal V. Veeravalli

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Information-theoretic bounds in learning algorithms

Yuheng Bu

PhD thesis, 2019

Linear-Complexity Exponentially-Consistent Tests for Universal Outlying Sequence Detection.

Yuheng Bu, Shaofeng Zou, Venugopal V. Veeravalli

IEEE Trans. Signal Process., 2019

Adaptive Sequential Machine Learning.

Craig Wilson, Yuheng Bu, Venugopal V. Veeravalli

CoRR, 2019

Model Change Detection with Application to Machine Learning.

Yuheng Bu, Jiaxun Lu, Venugopal V. Veeravalli

Proceedings of the IEEE International Conference on Acoustics, 2019

Active and Adaptive Sequential Learning with Per Time-step Excess Risk Guarantees.

Yuheng Bu, Jiaxun Lu, Venugopal V. Veeravalli

Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019

2018

Estimation of KL Divergence: Optimal Minimax Rate.

Yuheng Bu, Shaofeng Zou, Yingbin Liang, Venugopal V. Veeravalli

IEEE Trans. Inf. Theory, 2018

Active Learning in Recommendation Systems with Multi-level User Preferences.

Yuheng Bu, Kevin Small

CoRR, 2018

Active and Adaptive Sequential learning.

Yuheng Bu, Jiaxun Lu, Venugopal V. Veeravalli

CoRR, 2018

2017

Linear Complexity Exponentially Consistent Tests for Outlying Sequence Detection.

Yuheng Bu, Shaofeng Zou, Venugopal V. Veeravalli

CoRR, 2017

2016

Estimation of KL divergence between large-alphabet distributions.

Yuheng Bu, Shaofeng Zou, Yingbin Liang, Venugopal V. Veeravalli

Proceedings of the IEEE International Symposium on Information Theory, 2016

Universal outlying sequence detection for continuous observations.

Yuheng Bu, Shaofeng Zou, Yingbin Liang, Venugopal V. Veeravalli

Proceedings of the 2016 IEEE International Conference on Acoustics, 2016