Yuheng Bu's Homepage
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Yuheng BuAssistant Professor Contact InformationEmail: buyuheng [at] {ufl} [dot] edu |
Biography
I am an Assistant Professor in the Department of Electrical & Computer Engineering (ECE) at University of Florida.
Before joining the University of Florida, I was a postdoctoral research associate at the Research Laboratory of Electronics and Institute for Data, Systems, and Society (IDSS), Massachusetts Institute of Technology (MIT). I received my Ph.D. degree at the Coordinated Science Laboratory and the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign (UIUC) in 2019. Before that, I received a B.S. degree (with honors) in Electronic Engineering from Tsinghua University in 2014.
Research
My research interests lie in the intersection of machine learning, information theory and signal processing. I leverage the tools from information theory and signal processing to develop theoretically justified learning algorithms for diverse applications, including fair machine learning, uncertainty quantification, model compression, and anomaly detection.
More broadly, the primary goal of my research is to lay information-theoretic foundations for trustworthy learning algorithms, particularly with generalization, fairness, and robustness guarantees.
To Prospective Students
PhD candidates: Self-motivated students with strong backgrounds in probability, statistics, machine learning, and information theory are welcome to reach out. You should be comfortable with writing and reading proofs and learning challenging new mathematical concepts; in other words, a decent mathematical maturity is required.
Please send me an email with the subject “Prospective Ph.D. Student” attached with your academic CV and undergraduate/graduate transcripts.
Selected Publications
M. Shen, Y. Bu, G. W. Wornell. “On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation,” International Conference on Machine Learning (ICML), Jul. 2023.
A. Shah*, Y. Bu*, J. K. Lee, P. Sattigeri, R. Panda, S. Das, G. W. Wornell. “Selective Regression under Fairness Criteria,” (* equal contribution), International Conference on Machine Learning (ICML), Jul. 2022.
Y. Bu*, G. Aminian*, L. Toni, M. R. D. Rodrigues, G. W. Wornell. “Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm,” (* equal contribution), International Conference on Artificial Intelligence and Statistics (AISTATS), Mar. 2022.
G. Aminian*, Y. Bu*, L. Toni, M. R. D. Rodrigues, G. W. Wornell. “An Exact Characterization of the Generalization Error for the Gibbs Algorithm,” (* equal contribution), Conference on Neural Information Processing Systems (NeurIPS), 2021.
Y. Bu*, J. K. Lee*, D. Rajan, P. Sattigeri, R. Panda, S. Das, G. W. Wornell. “Fair Selective Classification via Sufficiency,” (* equal contribution), International Conference on Machine Learning (ICML), Jul. 2021. (Long Talk, Top 3%)
Y. Bu, S. Zou, V. V. Veeravalli. “Tightening Mutual Information Based Bounds on Generalization Error,” IEEE Journal on Selected Areas in Information Theory, vol. 1, pp. 121 - 130, May 2020.
Y. Bu, S. Zou, V. V. Veeravalli. “Linear-Complexity Exponentially-Consistent Tests for Universal Outlying Sequence Detection,” IEEE Transactions on Signal Processing, vol. 67, no. 8, pp. 2115-2128, Apr. 2019.
Y. Bu*, S. Zou*, Y. Liang, V. V. Veeravalli. “Estimation of KL Divergence: Optimal Minimax Rate,” (*equal contribution), IEEE Transactions on Information Theory, vol. 64, no. 4, pp. 2648-2674, Apr. 2018.
News
March 2024, I will present our paper “Adaptive Text Watermark for Large Language Models” at Annual Conference on Information Science and Systems (CISS), Princeton, NJ.
Jan 2024, I have co-organized the 7th Annual Workshop on Cognition & Control together with Prof. Sean Meyn, Prof. Jie Fu at UF Reitz Union. Talks include topics such as reinforcement learning, accelerated optimization, information theory, brain-machine interface systems, and much more!
Jan 2024, our paper, “Gibbs-Based Information Criteria and the Over-Parameterized Regime” has been accepted by AISTATS 2024 (acceptance rate: 28%)! This is my student Haobo's first paper, Congrats to Haobo!
Oct 2023, our paper, “Information-theoretic Characterizations of Generalization Error for the Gibbs Algorithm” has been accepted for publication on IEEE Transactions on Information Theory.
Sep 2023, I gave an invited talk on “Group Fairness with Uncertainty in Sensitive Attributes” at Allerton Conference on Comminication, Control and Computing, Monticello, IL.
Aug 2023, the video recordings of the 6th Annual Workshop on Cognition & Control are now available on Youtube!
Apr 2023, our paper, “On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation,” has been accepted by ICML 2023 (acceptance rate: 28%).
Apr 2023, two papers, “On the Generalization Error of Meta Learning for the Gibbs Algorithm” and “A Bilateral Bound on the Mean Squared Error for Estimation in Model Mismatch,” have been accepted by ISIT 2023.
Feb 2023, my collaborators and I were interviewed with MIT News for our recent AAAI publication “Post-hoc Uncertainty Learning using a Dirichlet Meta-Model.” Check out here!
Feb 2023, I gave a talk on “Characterizing the generalization error of Gibbs algorithms using information measures” at Information Theory and Applications Workshop, ITA 2023, San Diego.
Jan 2023, our paper, “Adaptive Sequential Machine Learning,” has been selected as the winner of the 16th Abraham Wald Prize in Sequential Analysis!
Jan 2023, I have co-organized the 6th Annual Workshop on Cognition & Control together with Prof. Sean Meyn, Prof. Shreya Saxena at UF Reitz Union. Talks include topics such as reinforcement learning, accelerated optimization, information theory, brain-machine interface systems, and much more!
Jan 2023, I gave a virtual presentation “From Sensitivity-constrained Information Bottleneck to Fair Selective Prediction” at Information Theory and Data Science Workshop organized by National University of Singapore.
Jan 2023, our paper, “Reliable Gradient-free and Likelihood-free Prompt Tuning,” has been accepted by Findings of EACL 2023!
Jan 2023, our paper, “How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm?” has been accepted by AISTATS 2023 (acceptance rate: 29%)!
Nov 2022, our paper, “Post-hoc Uncertainty Learning using a Dirichlet Meta-Model,” has been accepted by AAAI 2023 (acceptance rate: 19%)!
Aug 2022, our paper, “Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation,” has been awarded as Best Student Paper by IEEE MLSP 2022.
Aug 2022, Dr. Prasanna Sattigeri and I are serving as Guest Editors for the Entropy Special Issue “Fairness in Machine Learning: Information Theoretic Perspectives.” We warmly invite you to contribute an article or extended conference paper for this Special Issue.
Aug 2022, our paper, “Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals,” has been accepted by GLOBECOM 2022.