Publications
Journal Publications
Informationtheoretic Characterizations of Generalization Error for the Gibbs Algorithm
G. Aminian*, Y. Bu*, L. Toni, M. R. Rodrigues, G. W. Wornell (* equal contribution)
IEEE Transactions on Information Theory, vol. 70, no. 1, pp. 632655, Jan. 2024.
A Maximal Correlation Framework for Fair Machine Learning
Y. Bu*, J. K. Lee*, P. Sattigeri, R. Panda, G. W. Wornell, L. Karlinsky, R. S. Feris (* equal contribution)
Entropy 24, no. 4, pp. 461, Mar. 2022.
Population Risk Improvement with Model Compression: An InformationTheoretic Approach
Y. Bu, W. Gao, S. Zou, V. V. Veeravalli.
Entropy 23, no. 10, pp. 1255, Sept. 2021.
Tightening Mutual Information Based Bounds on Generalization Error
Y. Bu, S. Zou, V. V. Veeravalli.
IEEE Journal on Selected Areas in Information Theory, vol. 1, pp. 121  130, May 2020.
Adaptive Sequential Machine Learning
C. Wilson, Y. Bu, V. V. Veeravalli.
Sequential Analysis, vol. 38, no. 4, Oct. 2019. (16th Abraham Wald Prize)
LinearComplexity ExponentiallyConsistent Tests for Universal Outlying Sequence Detection
Y. Bu, S. Zou, V. V. Veeravalli.
IEEE Transactions on Signal Processing, vol. 67, no. 8, pp. 21152128, Apr. 2019.
Estimation of KL Divergence: Optimal Minimax Rate
Y. Bu*, S. Zou*, Y. Liang, V. V. Veeravalli. (*equal contribution),
IEEE Transactions on Information Theory, vol. 64, no. 4, pp. 26482674, Apr. 2018.
Code available at Github
Conference and Workshop Publications
2024
Informationtheoretic Analysis of the Gibbs Algorithm: An Individual Sample Approach
Y. Zhu, Y. Bu
to appear in, IEEE Information Theory Workshop (ITW), Shenzhen, China, Nov. 2024.
Learning Orthonormal Features in SelfSupervised Learning using Functional Maximal Correlation
B. Hu, Y. Bu, J. C. PrÃncipe
to appear in, IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, Oct. 2024.
Short version at
NeurIPS Workshop on SelfSupervised Learning  Theory and Practice, New Orleans, LA, Dec. 2023. (Spotlight talk)
Operator SVD with Neural Networks via Nested LowRank Approximation
J. J. Ryu, X. Xu, H. SM Erol, Y. Bu, L. Zheng, G. W. Wornell
International Conference on Machine Learning (ICML), Vienna, Austria, Jul. 2024. (acceptance rate: 28%)
Code available at Github
Short version at NeurIPS Workshop on Machine Learning and the Physical Sciences, New Orleans, LA, Dec. 2023.
Group Fairness with Uncertainty in Sensitive Attributes
A. Shah, M. Shen, J. J. Ryu, S. Das, P. Sattigeri, Y. Bu, G. W. Wornell
IEEE International Symposium on Information Theory (ISIT), Athens, Greece, Jul. 2024.
Full version at ICML workshop on Spurious Correlations, Invariance and Stability, Honolulu, HI, Jul. 2023.
2023
2022
Selective Regression under Fairness Criteria
A. Shah*, Y. Bu*, J. K. Lee, P. Sattigeri, R. Panda, S. Das, G. W. Wornell (* equal contribution)
International Conference on Machine Learning (ICML), Baltimore, MD, Jul. 2022. (acceptance rate: 22%)
2021
Before 2021
