Yuheng Bu - RankMe

95 papers A* 13A 3B 12Misc 4Journal 56Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	—	`conf`	A Reinforcement Learning Framework for Robust and Secure LLM Watermarking. EACL (Volume 1: Long Papers) Li An, Yujian Liu, Yepeng Liu, Yuheng Bu, Yang Zhang, Shiyu Chang
2026	J	`jnl`	ConvexBench: Can LLMs Recognize Convex Functions? CoRR Yepeng Liu, Yu Huang, Yu-Xiang Wang, Yingbin Liang, Yuheng Bu
2026	A*	`conf`	TrustEnergy: A Unified Framework for Accurate and Reliable User-level Energy Usage Prediction. AAAI Dahai Yu, Rongchao Xu, Dingyi Zhuang, Yuheng Bu, Shenhao Wang, Guang Wang
2026	J	`jnl`	TrustEnergy: A Unified Framework for Accurate and Reliable User-level Energy Usage Prediction. CoRR Dahai Yu, Rongchao Xu, Dingyi Zhuang, Yuheng Bu, Shenhao Wang, Guang Wang
2025	J	`jnl`	A Reinforcement Learning Framework for Robust and Secure LLM Watermarking. CoRR Li An, Yujian Liu, Yepeng Liu, Yuheng Bu, Yang Zhang, Shiyu Chang
2025	J	`jnl`	Class-wise Generalization Error: an Information-Theoretic analysis. Trans. Mach. Learn. Res. Firas Laakom, Moncef Gabbouj, Jürgen Schmidhuber, Yuheng Bu
2025	J	`jnl`	Dataset Protection via Watermarked Canaries in Retrieval-Augmented LLMs. CoRR Yepeng Liu, Xuandong Zhao, Dawn Song, Yuheng Bu
2025	J	`jnl`	Defending LLM Watermarking Against Spoofing Attacks with Contrastive Representation Learning. CoRR Li An, Yujian Liu, Yepeng Liu, Yang Zhang, Yuheng Bu, Shiyu Chang
2025	J	`jnl`	Distributional Information Embedding: A Framework for Multi-bit Watermarking. CoRR Haiyun He, Yepeng Liu, Ziqiao Wang, Yongyi Mao, Yuheng Bu
2025	A*	`conf`	Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective. ICML Firas Laakom, Haobo Chen, Jürgen Schmidhuber, Yuheng Bu
2025	J	`jnl`	Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective. CoRR Firas Laakom, Haobo Chen, Jürgen Schmidhuber, Yuheng Bu
2025	J	`jnl`	Graph neural networks for residential location choice: connection to classical logit models. CoRR Zhanhong Cheng, Lingqian Hu, Yuheng Bu, Yuqi Zhou, Shenhao Wang
2025	A*	`conf`	Image Watermarks are Removable using Controllable Regeneration from Clean Noise. ICLR Yepeng Liu, Yiren Song, Hai Ci, Yu Zhang, Haofan Wang, Mike Zheng Shou, Yuheng Bu
2025	J	`jnl`	In-Context Watermarks for Large Language Models. CoRR Yepeng Liu, Xuandong Zhao, Christopher Kruegel, Dawn Song, Yuheng Bu
2025	J	`jnl`	NestGNN: A Graph Neural Network Framework Generalizing the Nested Logit Model for Travel Mode Choice. CoRR Yuqi Zhou, Zhanhong Cheng, Lingqian Hu, Yuheng Bu, Shenhao Wang
2025	J	`jnl`	Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption. CoRR Yepeng Liu, Xuandong Zhao, Dawn Song, Gregory W. Wornell, Yuheng Bu
2025	J	`jnl`	SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting. CoRR Yang Xing, Jiong Wu, Yuheng Bu, Kuang Gong
2025	—	`conf`	UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction. SIGSPATIAL/GIS Dahai Yu, Dingyi Zhuang, Lin Jiang, Rongchao Xu, Xinyue Ye, Yuheng Bu, Shenhao Wang, Guang Wang
2025	J	`jnl`	UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction. CoRR Dahai Yu, Dingyi Zhuang, Lin Jiang, Rongchao Xu, Xinyue Ye, Yuheng Bu, Shenhao Wang, Guang Wang
2024	A*	`conf`	Adaptive Text Watermark for Large Language Models. ICML Yepeng Liu, Yuheng Bu
2024	J	`jnl`	Adaptive Text Watermark for Large Language Models. CoRR Yepeng Liu, Yuheng Bu
2024	—	`conf`	An Algorithm for Computing the Capacity of Symmetrized KL Information for Discrete Channels. Allerton Haobo Chen, Gholamali Aminian, Yuheng Bu
2024	J	`jnl`	An Algorithm for Computing the Capacity of Symmetrized KL Information for Discrete Channels. CoRR Haobo Chen, Gholamali Aminian, Yuheng Bu
2024	A*	`conf`	Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage? NeurIPS Maohao Shen, Jongha Jon Ryu, Soumya Ghosh, Yuheng Bu, Prasanna Sattigeri, Subhro Das, Gregory W. Wornell
2024	J	`jnl`	Class-wise Generalization Error: an Information-Theoretic Analysis. CoRR Firas Laakom, Yuheng Bu, Moncef Gabbouj
2024	A	`conf`	Gibbs-Based Information Criteria and the Over-Parameterized Regime. AISTATS Haobo Chen, Gregory W. Wornell, Yuheng Bu
2024	B	`conf`	Group Fairness with Uncertain Sensitive Attributes. ISIT Abhin Shah, Maohao Shen, Jongha Jon Ryu, Subhro Das, Prasanna Sattigeri, Yuheng Bu, Gregory W. Wornell
2024	J	`jnl`	Image Watermarks are Removable Using Controllable Regeneration from Clean Noise. CoRR Yepeng Liu, Yiren Song, Hai Ci, Yu Zhang, Haofan Wang, Mike Zheng Shou, Yuheng Bu
2024	J	`jnl`	Improved Evidential Deep Learning via a Mixture of Dirichlet Distributions. CoRR Jongha Jon Ryu, Maohao Shen, Soumya Ghosh, Yuheng Bu, Prasanna Sattigeri, Subhro Das, Gregory W. Wornell
2024	B	`conf`	Information-Theoretic Analysis of the Gibbs Algorithm: An Individual Sample Approach. ITW Youheng Zhu, Yuheng Bu
2024	J	`jnl`	Information-Theoretic Characterizations of Generalization Error for the Gibbs Algorithm. IEEE Trans. Inf. Theory Gholamali Aminian, Yuheng Bu, Laura Toni, Miguel R. D. Rodrigues, Gregory W. Wornell
2024	A*	`conf`	Information-Theoretic Opacity-Enforcement in Markov Decision Processes. IJCAI Chongyang Shi, Yuheng Bu, Jie Fu
2024	J	`jnl`	Information-Theoretic Opacity-Enforcement in Markov Decision Processes. CoRR Chongyang Shi, Yuheng Bu, Jie Fu
2024	J	`jnl`	Information-theoretic Analysis of the Gibbs Algorithm: An Individual Sample Approach. CoRR Youheng Zhu, Yuheng Bu
2024	B	`conf`	Learning Orthonormal Features in Self-Supervised Learning using Functional Maximal Correlation. ICIP Bo Hu, Yuheng Bu, José C. Príncipe
2024	A*	`conf`	Operator SVD with Neural Networks via Nested Low-Rank Approximation. ICML Jongha Jon Ryu, Xiangxiang Xu, Hasan Sabri Melihcan Erol, Yuheng Bu, Lizhong Zheng, Gregory W. Wornell
2024	J	`jnl`	Operator SVD with Neural Networks via Nested Low-Rank Approximation. CoRR Jongha Jon Ryu, Xiangxiang Xu, H. S. Melihcan Erol, Yuheng Bu, Lizhong Zheng, Gregory W. Wornell
2024	—	`conf`	SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks. SIGSPATIAL/GIS Dingyi Zhuang, Yuheng Bu, Guang Wang, Shenhao Wang, Jinhua Zhao
2024	J	`jnl`	SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks. CoRR Dingyi Zhuang, Yuheng Bu, Guang Wang, Shenhao Wang, Jinhua Zhao
2024	B	`conf`	Towards Optimal Inverse Temperature in the Gibbs Algorithm. ISIT Yuheng Bu
2024	J	`jnl`	Universally Optimal Watermarking Schemes for LLMs: from Theory to Practice. CoRR Haiyun He, Yepeng Liu, Ziqiao Wang, Yongyi Mao, Yuheng Bu
2023	B	`conf`	A Bilateral Bound on the Mean-Square Error for Estimation in Model Mismatch. ISIT Amir Weiss, Alejandro Lancho, Yuheng Bu, Gregory W. Wornell
2023	J	`jnl`	A Bilateral Bound on the Mean-Square Error for Estimation in Model Mismatch. CoRR Amir Weiss, Alejandro Lancho, Yuheng Bu, Gregory W. Wornell
2023	J	`jnl`	Feature Learning in Image Hierarchies using Functional Maximal Correlation. CoRR Bo Hu, Yuheng Bu, José C. Príncipe
2023	J	`jnl`	Group Fairness with Uncertainty in Sensitive Attributes. CoRR Abhin Shah, Maohao Shen, Jongha Jon Ryu, Subhro Das, Prasanna Sattigeri, Yuheng Bu, Gregory W. Wornell
2023	A	`conf`	How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm? AISTATS Haiyun He, Gholamali Aminian, Yuheng Bu, Miguel R. D. Rodrigues, Vincent Y. F. Tan
2023	A*	`conf`	On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation. ICML Maohao Shen, Yuheng Bu, Gregory W. Wornell
2023	B	`conf`	On the Generalization Error of Meta Learning for the Gibbs Algorithm. ISIT Yuheng Bu, Harsha Vardhan Tetali, Gholamali Aminian, Miguel R. D. Rodrigues, Gregory W. Wornell
2023	J	`jnl`	On the Generalization Error of Meta Learning for the Gibbs Algorithm. CoRR Yuheng Bu, Harsha Vardhan Tetali, Gholamali Aminian, Miguel R. D. Rodrigues, Gregory W. Wornell
2023	A*	`conf`	Post-hoc Uncertainty Learning Using a Dirichlet Meta-Model. AAAI Maohao Shen, Yuheng Bu, Prasanna Sattigeri, Soumya Ghosh, Subhro Das, Gregory W. Wornell
2023	—	`conf`	Reliable Gradient-free and Likelihood-free Prompt Tuning. EACL (Findings) Maohao Shen, Soumya Ghosh, Prasanna Sattigeri, Subhro Das, Yuheng Bu, Gregory W. Wornell
2023	J	`jnl`	Reliable Gradient-free and Likelihood-free Prompt Tuning. CoRR Maohao Shen, Soumya Ghosh, Prasanna Sattigeri, Subhro Das, Yuheng Bu, Gregory W. Wornell
2023	J	`jnl`	SGLD-Based Information Criteria and the Over-Parameterized Regime. CoRR Haobo Chen, Yuheng Bu, Gregory W. Wornell
2022	Misc	`conf`	A Maximal Correlation Approach to Imposing Fairness in Machine Learning. ICASSP Joshua K. Lee, Yuheng Bu, Prasanna Sattigeri, Rameswar Panda, Gregory W. Wornell, Leonid Karlinsky, Rogério Feris
2022	J	`jnl`	A Maximal Correlation Framework for Fair Machine Learning. Entropy Joshua K. Lee, Yuheng Bu, Prasanna Sattigeri, Rameswar Panda, Gregory W. Wornell, Leonid Karlinsky, Rogério Schmidt Feris
2022	A	`conf`	Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm. AISTATS Yuheng Bu, Gholamali Aminian, Laura Toni, Gregory W. Wornell, Miguel R. D. Rodrigues
2022	B	`conf`	Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals. GLOBECOM Alejandro Lancho, Amir Weiss, Gary C. F. Lee, Jennifer Tang, Yuheng Bu, Yury Polyanskiy, Gregory W. Wornell
2022	J	`jnl`	Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals. CoRR Alejandro Lancho, Amir Weiss, Gary C. F. Lee, Jennifer Tang, Yuheng Bu, Yury Polyanskiy, Gregory W. Wornell
2022	—	`conf`	Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation. MLSP Gary C. F. Lee, Amir Weiss, Alejandro Lancho, Jennifer Tang, Yuheng Bu, Yury Polyanskiy, Gregory W. Wornell
2022	J	`jnl`	Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation. CoRR Gary C. F. Lee, Amir Weiss, Alejandro Lancho, Jennifer Tang, Yuheng Bu, Yury Polyanskiy, Gregory W. Wornell
2022	J	`jnl`	How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm? CoRR Haiyun He, Gholamali Aminian, Yuheng Bu, Miguel R. D. Rodrigues, Vincent Y. F. Tan
2022	J	`jnl`	Information-theoretic Characterizations of Generalization Error for the Gibbs Algorithm. CoRR Gholamali Aminian, Yuheng Bu, Laura Toni, Miguel R. D. Rodrigues, Gregory W. Wornell
2022	J	`jnl`	On the Benefits of Selectivity in Pseudo-Labeling for Unsupervised Multi-Source-Free Domain Adaptation. CoRR Maohao Shen, Yuheng Bu, Gregory W. Wornell
2022	J	`jnl`	Post-hoc Uncertainty Learning using a Dirichlet Meta-Model. CoRR Maohao Shen, Yuheng Bu, Prasanna Sattigeri, Soumya Ghosh, Subhro Das, Gregory W. Wornell
2022	A*	`conf`	Selective Regression under Fairness Criteria. ICML Abhin Shah, Yuheng Bu, Joshua K. Lee, Subhro Das, Rameswar Panda, Prasanna Sattigeri, Gregory W. Wornell
2022	B	`conf`	Tighter Expected Generalization Error Bounds via Convexity of Information Measures. ISIT Gholamali Aminian, Yuheng Bu, Gregory W. Wornell, Miguel R. D. Rodrigues
2022	J	`jnl`	Tighter Expected Generalization Error Bounds via Convexity of Information Measures. CoRR Gholamali Aminian, Yuheng Bu, Gregory W. Wornell, Miguel R. D. Rodrigues
2021	A*	`conf`	An Exact Characterization of the Generalization Error for the Gibbs Algorithm. NeurIPS Gholamali Aminian, Yuheng Bu, Laura Toni, Miguel R. D. Rodrigues, Gregory W. Wornell
2021	J	`jnl`	Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm. CoRR Yuheng Bu, Gholamali Aminian, Laura Toni, Miguel R. D. Rodrigues, Gregory W. Wornell
2021	J	`jnl`	Characterizing the Generalization Error of Gibbs Algorithm with Symmetrized KL information. CoRR Gholamali Aminian, Yuheng Bu, Laura Toni, Miguel R. D. Rodrigues, Gregory W. Wornell
2021	A*	`conf`	Fair Selective Classification Via Sufficiency. ICML Joshua K. Lee, Yuheng Bu, Deepta Rajan, Prasanna Sattigeri, Rameswar Panda, Subhro Das, Gregory W. Wornell
2021	J	`jnl`	Population Risk Improvement with Model Compression: An Information-Theoretic Approach. Entropy Yuheng Bu, Weihao Gao, Shaofeng Zou, Venugopal V. Veeravalli
2021	B	`conf`	SDP Methods for Sensitivity-Constrained Privacy Funnel and Information Bottleneck Problems. ISIT Yuheng Bu, Tony Wang, Gregory W. Wornell
2021	J	`jnl`	Selective Regression Under Fairness Criteria. CoRR Abhin Shah, Yuheng Bu, Joshua Ka-Wing Lee, Subhro Das, Rameswar Panda, Prasanna Sattigeri, Gregory W. Wornell
2020	J	`jnl`	A Maximal Correlation Approach to Imposing Fairness in Machine Learning. CoRR Joshua K. Lee, Yuheng Bu, Prasanna Sattigeri, Rameswar Panda, Gregory W. Wornell, Leonid Karlinsky, Rogério Feris
2020	A*	`conf`	Information-Theoretic Understanding of Population Risk Improvement with Model Compression. AAAI Yuheng Bu, Weihao Gao, Shaofeng Zou, Venugopal V. Veeravalli
2020	J	`jnl`	Tightening Mutual Information-Based Bounds on Generalization Error. IEEE J. Sel. Areas Inf. Theory Yuheng Bu, Shaofeng Zou, Venugopal V. Veeravalli
2019	Misc	`conf`	Active and Adaptive Sequential Learning with Per Time-step Excess Risk Guarantees. ACSSC Yuheng Bu, Jiaxun Lu, Venugopal V. Veeravalli
2019	J	`jnl`	Adaptive Sequential Machine Learning. CoRR Craig Wilson, Yuheng Bu, Venugopal V. Veeravalli
2019	J	`jnl`	Information-Theoretic Understanding of Population Risk Improvement with Model Compression. CoRR Yuheng Bu, Weihao Gao, Shaofeng Zou, Venugopal V. Veeravalli
2019	—	`—`	Information-theoretic bounds in learning algorithms Yuheng Bu
2019	J	`jnl`	Linear-Complexity Exponentially-Consistent Tests for Universal Outlying Sequence Detection. IEEE Trans. Signal Process. Yuheng Bu, Shaofeng Zou, Venugopal V. Veeravalli
2019	Misc	`conf`	Model Change Detection with Application to Machine Learning. ICASSP Yuheng Bu, Jiaxun Lu, Venugopal V. Veeravalli
2019	B	`conf`	Tightening Mutual Information Based Bounds on Generalization Error. ISIT Yuheng Bu, Shaofeng Zou, Venugopal V. Veeravalli
2019	J	`jnl`	Tightening Mutual Information Based Bounds on Generalization Error. CoRR Yuheng Bu, Shaofeng Zou, Venugopal V. Veeravalli
2018	J	`jnl`	Active Learning in Recommendation Systems with Multi-level User Preferences. CoRR Yuheng Bu, Kevin Small
2018	J	`jnl`	Active and Adaptive Sequential learning. CoRR Yuheng Bu, Jiaxun Lu, Venugopal V. Veeravalli
2018	J	`jnl`	Estimation of KL Divergence: Optimal Minimax Rate. IEEE Trans. Inf. Theory Yuheng Bu, Shaofeng Zou, Yingbin Liang, Venugopal V. Veeravalli
2018	J	`jnl`	Model change detection with application to machine learning. CoRR Yuheng Bu, Jiaxun Lu, Venugopal V. Veeravalli
2017	J	`jnl`	Linear Complexity Exponentially Consistent Tests for Outlying Sequence Detection. CoRR Yuheng Bu, Shaofeng Zou, Venugopal V. Veeravalli
2017	B	`conf`	Linear-complexity exponentially-consistent tests for universal outlying sequence detection. ISIT Yuheng Bu, Shaofeng Zou, Venugopal V. Veeravalli
2016	J	`jnl`	Estimation of KL Divergence: Optimal Minimax Rate. CoRR Yuheng Bu, Shaofeng Zou, Yingbin Liang, Venugopal V. Veeravalli
2016	B	`conf`	Estimation of KL divergence between large-alphabet distributions. ISIT Yuheng Bu, Shaofeng Zou, Yingbin Liang, Venugopal V. Veeravalli
2016	Misc	`conf`	Universal outlying sequence detection for continuous observations. ICASSP Yuheng Bu, Shaofeng Zou, Yingbin Liang, Venugopal V. Veeravalli
2015	J	`jnl`	Universal Outlying sequence detection For Continuous Observations. CoRR Yuheng Bu, Shaofeng Zou, Yingbin Liang, Venugopal V. Veeravalli