Published in , 2025
We provide an information-theoretic characterization of the separation-utility trade-off, yielding a principled Pareto frontier perspective for designing and evaluating separation-based objectives.
Recommended citation: Shizhou Xu. (2025). “Utility–Separation Pareto Frontier: An Information-Theoretic Characterization.”
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Published in Under review at Nature Machine Intelligence, 2025
We develop multi-resolution enhancement strategies for full-spectrum neural representations, improving fidelity across scales with an emphasis on robust learning and generalization.
Recommended citation: Yuan Ni, Z. Chen, Shizhou Xu, C. Peng, R. Plumley, C. H. Yoon, J. Thayer, J. Turner. (2025). “Multi-resolution Enhancement for Full Spectrum Neural Representations.” Under review at Nature Machine Intelligence.
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Published in Manuscript, 2025
We develop information-theoretic regularization principles for machine unlearning, aiming to remove targeted information while maintaining general utility and enabling principled evaluation.
Recommended citation: Shizhou Xu, Thomas Strohmer. (2025). “Machine Unlearning via Information-Theoretic Regularization.” under review at Mathematical Foundations of Machine Learning.
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Published in Under review, 2025
We propose marginal-information regularization for LLM unlearning, targeting targeted forgetting with strong utility retention and practical, evaluation-driven design.
Recommended citation: Shizhou Xu, Yuan Ni, Stefan Broecker, Thomas Strohmer. (2025). “Forgetting-MarI: LLM Unlearning via Marginal Information Regularization.” Under review.
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Published in Under review (Journal of the American Statistical Association), 2024
We introduce WHOMP, a Wasserstein-homogeneity optimality principle for subgroup splitting in comparative experiments (clinical trials, social experiments, and A/B tests). The method yields interpretable criteria, efficient estimators, and strong empirical gains over random partitioning, covariate-adaptive randomization, rerandomization, and anti-clustering baselines.
Recommended citation: Shizhou Xu, Thomas Strohmer. (2024). “WHOMP: Improving Upon Randomized Controlled Trials via Wasserstein Homogeneity.” Under review at Journal of the American Statistical Association.
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Published in Under review (SIAM Journal on Mathematics of Data Science — SIMODS), 2024
We analyze fundamental tensions between individual and group fairness notions, clarifying when they can or cannot be simultaneously satisfied and what trade-offs are unavoidable.
Recommended citation: Shizhou Xu, Thomas Strohmer. (2024). “On the (In)Compatibility between Individual and Group Fairness.” Under review at SIAM Journal on Mathematics of Data Science.
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