Publications

Journal Publications

1. Information-theoretic 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. 632-655, Jan. 2024.
   

2. 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.
   

3. Population Risk Improvement with Model Compression: An Information-Theoretic Approach
   Y. Bu, W. Gao, S. Zou, V. V. Veeravalli.
   Entropy 23, no. 10, pp. 1255, Sept. 2021.
   

4. 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.
   

5. Adaptive Sequential Machine Learning
   C. Wilson, Y. Bu, V. V. Veeravalli.
   Sequential Analysis, vol. 38, no. 4, Oct. 2019. (16th Abraham Wald Prize)
   

6. Linear-Complexity Exponentially-Consistent Tests for Universal Outlying Sequence Detection
   Y. Bu, S. Zou, V. V. Veeravalli.
   IEEE Transactions on Signal Processing, vol. 67, no. 8, pp. 2115-2128, Apr. 2019.
   

7. 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. 2648-2674, Apr. 2018.
   Code available at Github

Conference and Workshop Publications

2024

• Adaptive Text Watermark for Large Language Models
  Y. Liu Y. Bu
  Preprint

• Improved Evidential Deep Learning via a Mixture of Dirichlet Distributions
  J. J. Ryu, M. Shen, S. Ghosh, Y. Bu, P. Sattigeri, S. Das, G. W. Wornell
  Preprint

• Operator SVD with Neural Networks via Nested Low-Rank Approximation
  J. J. Ryu, X. Xu, H. SM Erol, Y. Bu, L. Zheng, G. W. Wornell
  Short version at NeurIPS Workshop on Machine Learning and the Physical Sciences, New Orleans, LA, Dec. 2023.

• Class-wise Generalization Error: an Information-Theoretic Analysis
  F. Laakom, Y. Bu, M. Gabbouj
  Short version at ICML workshop on PAC-Bayes Meets Interactive Learning, Honolulu, HI, Jul. 2023.

• Gibbs-Based Information Criteria and the Over-Parameterized Regime
  H. Chen, G. W. Wornell, Y. Bu
  to appear in, International Conference on Artificial Intelligence and Statistics (AISTATS), Valencia, Spain, May 2024. (acceptance rate: 28%)
  Short version at NeurIPS Workshop on Mathematics of Modern Machine Learning (M3L), New Orleans, LA, Dec. 2023.

2023

• SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks
  D. Zhuang, Y. Bu, G. Wang, S. Wang, J. Zhao
  NeurIPS Workshop on Temporal Graph Learning (TGL), New Orleans, LA, Dec. 2023. (Spotlight talk)

• Feature Learning in Image Hierarchies using Functional Maximal Correlation
  B. Hu, Y. Bu, J. C. Príncipe
  NeurIPS Workshop on Self-Supervised Learning - Theory and Practice, New Orleans, LA, Dec. 2023. (Spotlight talk)

• On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation
  M. Shen, Y. Bu, G. W. Wornell
  International Conference on Machine Learning (ICML), Honolulu, HI, Jul. 2023. (acceptance rate: 28%)

• Group Fairness with Uncertainty in Sensitive Attributes
  A. Shah, M. Shen, J. J. Ryu, S. Das, P. Sattigeri, Y. Bu, G. W. Wornell
  ICML workshop on Spurious Correlations, Invariance and Stability, Honolulu, HI, Jul. 2023.

• On the Generalization Error of Meta Learning for the Gibbs Algorithm
  Y. Bu, H. V. Tetali, G. Aminian, M. R. Rodrigues, G. W. Wornell
  IEEE International Symposium on Information Theory (ISIT), Taipei, Jun. 2023.

• A Bilateral Bound on the Mean Squared Error for Estimation in Model Mismatch
  A. Weiss, A. Lancho, Y. Bu, G. W. Wornell
  IEEE International Symposium on Information Theory (ISIT), Taipei, Jun. 2023.

• Reliable Gradient-free and Likelihood-free Prompt Tuning
  M. Shen, S. S. Ghosh, P. Sattigeri, S. Das, Y. Bu, G. W. Wornell
  Findings of the European Chapter of the Association for Computational Linguistics (EACL), Dubrovnik, Croatia, May 2023.

• How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm?
  H. He, G. Aminian, Y. Bu, M. R. Rodrigues, V. YF Tan
  International Conference on Artificial Intelligence and Statistics (AISTATS), Valencia, Spain, Apr. 2023. (acceptance rate: 29%)

• Post-hoc Uncertainty Learning using a Dirichlet Meta-Model
  M. Shen, Y. Bu, P. Sattigeri, S. S. Ghosh, S. Das, G. W. Wornell
  AAAI Conference on Artificial Intelligence (AAAI), Washington DC, Feb. 2023. (acceptance rate: 19%)
  Code available at Github

2022

• Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals
  A. Lancho, A. Weiss, G. C. Lee, J. Tang, Y. Bu, Y. Polyanskiy, G. W. Wornell
  IEEE Global Communications Conference, Rio de Janeiro, Brazil, Dec. 2022.

• Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation
  G. C. Lee, A. Weiss, A. Lancho, J. Tang, Y. Bu, Y. Polyanskiy, G. W. Wornell
  IEEE International Workshop on Machine Learning for Signal Processing, Xi'an, China, Aug. 2022. (Best Student Paper)

• 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%)

• Tighter Expected Generalization Error Bounds via Convexity of Information Measures
  G. Aminian*, Y. Bu*, G. W. Wornell, M. R. Rodrigues (* equal contribution)
  IEEE International Symposium on Information Theory (ISIT), Espoo, Finland, Jun. 2022.

• A Maximal Correlation Approach to Imposing Fairness in Machine Learning
  Y. Bu*, J. K. Lee*, P. Sattigeri, R. Panda, G. W. Wornell, L. Karlinsky, R. S. Feris (*equal contribution)
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, May 2022.

• Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm
  Y. Bu*, G. Aminian*, L. Toni, M. R. Rodrigues, G. W. Wornell (* equal contribution)
  International Conference on Artificial Intelligence and Statistics (AISTATS), Mar. 2022. (acceptance rate: 29%)

2021

• An Exact Characterization of the Generalization Error for the Gibbs Algorithm
  G. Aminian*, Y. Bu*, L. Toni, M. R. Rodrigues, G. W. Wornell (* equal contribution)
  Conference on Neural Information Processing Systems (NeurIPS), Dec. 2021. (acceptance rate: 26%)

• Fair Selective Classification via Sufficiency
  Y. Bu*, J. K. Lee*, D. Rajan, P. Sattigeri, R. Panda, S. Das, G. W. Wornell. (* equal contribution)
  International Conference on Machine Learning (ICML), Jul. 2021. (Oral, Top 3%)

• Characterizing the Generalization Error of Gibbs Algorithm with Symmetrized KL information
  G. Aminian*, Y. Bu*, L. Toni, M. R. Rodrigues, G. W. Wornell (* equal contribution)
  ICML Workshop on Information-Theoretic Methods for Rigorous, Responsible, and Reliable Machine Learning, Jul. 2021.

• SDP Methods for Sensitivity-Constrained Privacy Funnel and Information Bottleneck Problems
  Y. Bu, T. Wang, G. W. Wornell
  IEEE International Symposium on Information Theory (ISIT), Melbourne, Australia, Jul. 2021.

Before 2021

• Information-theoretic Understanding of Population Risk Improvement with Model Compression
  Y. Bu, W. Gao, S. Zou, V. V. Veeravalli
  AAAI Conference on Artificial Intelligence (AAAI), New York, Feb. 2020. (acceptance rate: 21%)

• Active and Adaptive Sequential learning
  Y. Bu, J. Lu, V. V. Veeravalli
  arXiv preprint, 2020.
  

• Active Learning in Recommendation Systems with Multi-level User Preferences
  Y. Bu, K. Small
  AAAI Workshop on Interactive and Conversational Recommendation Systems (WICRS), New York, Feb. 2020.

• Active and Adaptive Sequential Learning with Per Time-step Excess Risk Guarantees
  Y. Bu, J. Lu, V. V. Veeravalli
  IEEE Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 2019.

• Tightening Mutual Information Based Bounds on Generalization Error
  Y. Bu, S. Zou, V. V. Veeravalli
  IEEE International Symposium on Information Theory (ISIT), Paris, France, Jul. 2019.

• Model Change Detection with Application to Machine Learning
  Y. Bu, J. Lu, V. V. Veeravalli
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 2019.

• Linear-Complexity Exponentially-Consistent Tests for Universal Outlying Sequence Detection
  Y. Bu, S. Zou, V. V. Veeravalli
  IEEE International Symposium on Information Theory (ISIT), Aachen, Germany, Jun. 2017.

• Estimation of KL Divergence between Large-Alphabet Distributions
  Y. Bu, S. Zou, Y. Liang, V. V. Veeravalli
  IEEE International Symposium on Information Theory (ISIT), Barcelona, Spain, Jul. 2016

• Universal Outlying Sequence Detection for Continuous Observations
  Y. Bu, S. Zou, Y. Liang, V. V. Veeravalli
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, Mar. 2016