Research

Welcome to my research webpage! Below, I list my publications and preprints, where PDF : paper, GitHub : software, BibTeX : bibtex, and * : equal contribution.

Statistics Papers

  • Y. Zhang and E. Candès. Posterior Conformal Prediction. Submitted to Journal of the American Statistical Association, 2024. PDF GitHub BibTeX

  • Y. Zhang and Q. Zhao. \(L^\infty\)- and \(L^2\)-Sensitivity Analysis for Causal Inference with Unmeasured Confounding. Under revision in Biometrika, 2024. PDF GitHub BibTeX

  • Y. Zhang and Q. Zhao. Multiple Conditional Randomization Tests for Lagged and Spillover Treatment Effects. Biometrika, 2024. PDF GitHub BibTeX

  • Y. Zhang and Q. Zhao. What is a Randomization Test?. Journal of the American Statistical Association, 118(544): 2928-2942, 2023. PDF GitHub BibTeX

  • Y. Zhang* and Z. Gao*. Causal Subgroup Discovery with Error Control. In preparation.

Machine Learning Papers

  • Y. Zhang*, J. Berrevoets*, and M. van der Schaar. Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects.
    International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 151:4158–4177, 2022. PDF GitHub BibTeX

  • T. Kyono*, Y. Zhang*, A. Bellot, and M. van der Schaar. MIRACLE: Causally-aware Imputation via Learning Missing Data Mechanisms.
    Advances in Neural Information Processing Systems (NeurIPS), 34:23806–23817, 2021. PDF GitHub BibTeX

  • Z. Qian, Y. Zhang, I. Bica, A. Wood, and M. van der Schaar. Synctwin: Treatment Effect Estimation with Longitudinal Outcomes.
    Advances in Neural Information Processing Systems (NeurIPS), 34:3178–3190, 2021. PDF GitHub BibTeX

  • Y. Zhang, A. Bellot, and M. van der Schaar. Learning Overlapping Representations for the Estimation of Individualized Treatment Effects.
    International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 108:1005–1014, 2020. PDF GitHub BibTeX

  • Y. Zhang, D. Jarrett, and M. van der Schaar. Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning.
    International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 108:2304–2314, 2020. PDF GitHub BibTeX

  • Y. Zhang and M. van der Schaar. Gradient Regularized \(V\)-Learning for Dynamic Treatment Regimes.
    Advances in Neural Information Processing Systems (NeurIPS), 33:2245–2256, 2020. PDF GitHub BibTeX

  • H. Lee*, Y. Zhang*, W. Zame, C. Shen, J. Lee, and M. van der Schaar. Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification. Advances in Neural Information Processing Systems (NeurIPS), 33:2282–2292, 2020. PDF GitHub BibTeX

  • T. Kyono*, Y. Zhang*, and M. van der Schaar. CASTLE: Regularization via Auxiliary Causal Graph Discovery.
    Advances in Neural Information Processing Systems (NeurIPS), 33:1501–1512, 2020. PDF GitHub BibTeX

  • J. Yoon, Y. Zhang, J. Jordon, and M. van der Schaar. VIME: Extending The Success of Self-and Semi-Supervised Learning to Tabular Domain.
    Advances in Neural Information Processing Systems (NeurIPS), 33:11033–11043, 2020. PDF GitHub BibTeX

  • J. Crabbe, Y. Zhang, and M. van der Schaar. Learning Outside the Black-box: the Pursuit of Interpretable Models.
    Advances in Neural Information Processing Systems (NeurIPS), 33:17838–17849, 2020. PDF GitHub BibTeX

Scientific Papers

  • S. Becker, Y. Zhang, and A. A. Lee. Geometry of Energy Landscapes and the Optimizability of Deep Neural Networks. Physical Review Letters, 124(10), 2020. PDF BibTeX

  • Y. Zhang, Q. Tang, Y. Zhang, J. Wang, U. Stimming, and A. A. Lee. Identifying Degradation Patterns of Li-ion Batteries from Impedance Spectroscopy using Machine Learning. Nature Communications, 11(1), 2020. PDF BibTeX

  • Y. Zhang and A. A. Lee. Bayesian Semi-supervised Learning for Uncertainty-calibrated Prediction of Molecular Properties and Active Learning.
    Chemical Science, 10(35), 8154-8163, 2019. PDF BibTeX

  • Y. Zhang, A. M. Saxe, M. S. Advani, and A. A. Lee. Energy–Entropy Competition and the Effectiveness of Stochastic Gradient Descent in Machine Learning.
    Molecular Physics, 116(21-22), 3214-3223, 2018. PDF BibTeX