Research

Conformalized Rate-Adaptive Sensing

J. Yang and Y. Zhang

Multi-Fidelity Quantile Regression

Y. Liu and Y. Zhang

Fit CATE Once: Model-Assisted Randomization Tests Without Sample Splitting

F. Zheng and Y. Zhang

Adaptive Sample Splitting for Randomization Tests

Y. Zhang and Z. Gao

Posterior Conformal Prediction

Y. Zhang and E. Candès

An average-case sensitivity analysis for unmeasured confounding

Y. Zhang and Q. Zhao

Biometrika, 113(2), 2026.

Multiple Conditional Randomization Tests for Lagged and Spillover Treatment Effects

Y. Zhang and Q. Zhao

Biometrika, 112(1): 3–16, 2025.

What is a Randomization Test?

Y. Zhang and Q. Zhao

Journal of the American Statistical Association, 118(544): 2928–2942, 2023.

Geometry of Energy Landscapes and the Optimizability of Deep Neural Networks

S. Becker, Y. Zhang, and A. A. Lee

Physical Review Letters, 124(10), 108301, 2020.

Identifying Degradation Patterns of Li-ion Batteries from Impedance Spectroscopy using Machine Learning

Y. Zhang, Q. Tang, Y. Zhang, J. Wang, U. Stimming, and A. A. Lee

Nature Communications, 11(1), 2020.

Bayesian Semi-supervised Learning for Uncertainty-calibrated Prediction of Molecular Properties and Active Learning

Y. Zhang and A. A. Lee

Chemical Science, 10(35), 8154–8163, 2019.

Energy–Entropy Competition and the Effectiveness of Stochastic Gradient Descent in Machine Learning

Y. Zhang, A. M. Saxe, M. S. Advani, and A. A. Lee

Molecular Physics, 116(21–22), 3214–3223, 2018.

Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects

Y. Zhang*, J. Berrevoets*, and M. van der Schaar

International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 151:4158–4177, 2022.

MIRACLE: Causally-aware Imputation via Learning Missing Data Mechanisms

T. Kyono*, Y. Zhang*, A. Bellot, and M. van der Schaar

Advances in Neural Information Processing Systems (NeurIPS), 34:23806–23817, 2021.

SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes

Z. Qian, Y. Zhang, I. Bica, A. Wood, and M. van der Schaar

Advances in Neural Information Processing Systems (NeurIPS), 34:3178–3190, 2021.

Learning Overlapping Representations for the Estimation of Individualized Treatment Effects

Y. Zhang, A. Bellot, and M. van der Schaar

International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 108:1005–1014, 2020.

Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning

Y. Zhang, D. Jarrett, and M. van der Schaar

International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 108:2304–2314, 2020.

Gradient Regularized V-Learning for Dynamic Treatment Regimes

Y. Zhang and M. van der Schaar

Advances in Neural Information Processing Systems (NeurIPS), 33:2245–2256, 2020.

Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification

H. Lee*, Y. Zhang*, W. Zame, C. Shen, J. Lee, and M. van der Schaar

Advances in Neural Information Processing Systems (NeurIPS), 33:2282–2292, 2020.

CASTLE: Regularization via Auxiliary Causal Graph Discovery

T. Kyono*, Y. Zhang*, and M. van der Schaar

Advances in Neural Information Processing Systems (NeurIPS), 33:1501–1512, 2020.

VIME: Extending the Success of Self- and Semi-Supervised Learning to Tabular Domain

J. Yoon, Y. Zhang, J. Jordon, and M. van der Schaar

Advances in Neural Information Processing Systems (NeurIPS), 33:11033–11043, 2020.

Learning Outside the Black-box: the Pursuit of Interpretable Models

J. Crabbé, Y. Zhang, and M. van der Schaar

Advances in Neural Information Processing Systems (NeurIPS), 33:17838–17849, 2020.