Journal Publications

• Improving knockoffs with conditional calibration, Yixiang Luo, William Fithian, and Lihua Lei, 2025. To appear in Annals of Statistics (AoS).

• Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control, Anastasios Angelopoulos, Stephen Bates, Emmanuel Candès, Michael I. Jordan, and Lihua Lei, 2025 Annals of Applied Statistics (AoAS), 19(2), 1641-1662. Arxiv

• Discussion of ”Data Fission: Splitting a Single Data Point” – Some Asymptotic Results for Data Fission. Lihua Lei, 2025, Journal of the American Statistical Association (JASA)}, 120(549), 147-150, (invited discussion paper). Arxiv

• Double-Robust Two-Way-Fixed-Effects Regression For Panel Data, Dmitry Arkhangelsky, Guido W. Imbens, Lihua Lei, and Xiaoman Luo, 2021. Quantitative Economics (QE), 15(4), 999-1034. Arxiv

• Adaptive novelty detection with false discovery rate guarantee, Ariane Marandon, Lihua Lei, David Mary, and Etienne Roquain, 2024. Annals of Statistics (AoS), 52(1), 157-183. Arxiv

• A Discussion of Tse and Davidson (2022)“A Note on Universal Inference”, Asher Spector, Lihua Lei, Emmanuel Candès, 2023. Stat

• Testing for Outliers with Conformal p-values, Stephen Bates, Emmanuel Candès, Lihua Lei, Yaniv Romano, and Matteo Sesia, 2023. Annals of Statistics (AoS), 51(1), 149-178.

• Conformalized Survival Analysis, Emmanuel Candès, Lihua Lei, and Zhimei Ren, 2022+. To appear in Journal of the Royal Statistical Society: Series B (JRSS-B).

• Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization, Samuel Horváth, Lihua Lei, Peter Richtárik, and Michael I. Jordan, 2022. SIAM Journal on Mathematics of Data Science (SIMODS), 4 (2022), 634-648. Arxiv

• Conditional calibration for false discovery rate control under dependence, William Fithian, and Lihua Lei, 2022. Annals of Statistics (AoS), 50(6), 3091-3118. Arxiv

• Distribution-Free, Risk-Controlling Prediction Sets, Stephen Bates, Anastasios Angelopoulos, Lihua Lei, Jitendra Malik, and Michael I. Jordan, 2021. Journal of the ACM, 68 (2021), 1-34. Arxiv

• Smoothed Nested Testing on Directed Acyclic Graphs, Jackson H. Loper, Lihua Lei, William Fithian, and Wesley Tansey, 2021. To appear in Biometrika. Arxiv

• Conformal Inference of Counterfactuals and Individual Treatment Effects, Lihua Lei, and Emmanuel Candès, 2021. Journal of the Royal Statistical Society: Series B (JRSS-B), 83, 911-938. Arxiv

• Regression adjustment in randomized experiments with a diverging number of covariates, Lihua Lei, and Peng Ding, 2020. Biometrika, 108, 815-828. Arxiv

• Hierarchical community detection by recursive partitioning, Tianxi Li, Lihua Lei, Sharmodeep Bhattacharyya, Koen Van den Berge, Purnamrita Sarkar, Peter J. Bickel, and Elizaveta Levina, 2020. Journal of the American Statistical Association (JASA), 117(538), 951–968. Arxiv

• An Assumption-Free Exact Test For Fixed-Design Linear Models With Exchangeable Errors, Lihua Lei, and Peter J. Bickel, 2021. Biometrika, 108, 397-412. Arxiv

• A general interactive framework for FDR control under structural constraints, Lihua Lei, Aaditya Ramdas, and William Fithian, 2021. Biometrika, 108, 253-267. Arxiv

• On the Adaptivity of Stochastic Gradient-Based Optimization, Lihua Lei, and Michael I. Jordan, 2020. SIAM Journal on Optimization (SIOPT), 30, 1473-1500. Arxiv

• Overlap in Observational Studies With High-Dimensional Covariates, Alexander D'Amour, Peng Ding, Avi Feller, Lihua Lei, and Jasjeet Sekhon, 2019. Journal of Econometrics (JoE), 221, 644-654. Arxiv

• AdaPT: An interactive procedure for multiple testing with side information, Lihua Lei, and William Fithian, 2018. Journal of the Royal Statistical Society: Series B (JRSS-B) 80 (2018), 649-679. Arxiv

• Asymptotics For High Dimensional Regression M-Estimates: Fixed Design Results, Lihua Lei, Peter J. Bickel, and Noureddine El Karoui. Probability Theory and Related Fields (PTRF), 172, 983-1079. Arxiv

• Functional Coefficient Moving Average Model With Applications To Forecasting Chinese CPI, Song Xi Chen, Lihua Lei, and Yundong Tu. Statistica Sinica, 26, 1649-1672.

Conference Publications

• Conformal Tail Risk Control for Large Language Model Alignment, Catherine Chen, Jingyan Shen, J., Zhun Deng, Lihua Lei, International Conference on Machine Learning (ICML), 2025.

• Total Variation Floodgate for Variable Importance Inference in Classification, Wenshuo Wang, Lucas Janson, Lihua Lei, Aaditya Ramdas, International Conference on Machine Learning (ICML), 2024.

• Conformal Risk Control Anastasios Angelopoulos, Stephen Bates, Adam Fisch, Lihua Lei, and Tal Schuster, International Conference on Learning Representations (ICLR), 2023.

• Variance Reduction with Sparse Gradients, Melih Elibol, Lihua Lei, Michael I. Jordan, International Conference on Learning Representations (ICLR), 2020.

• HONES: A Fast and Tuning-free Homotopy Method For Online Newton Step, Yuting Ye, Lihua Lei, and Cheng Ju, International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.

• Nonconvex Finite-Sum Optimization Via SCSG Methods, Lihua Lei, Cheng Ju, Jianbo Chen, and Michael I. Jordan. Advances in Neural Information Processing Systems (NIPS), 2017. Arxiv

• Less than a Single Pass: Stochastically Controlled Stochastic Gradient Method, Lihua Lei, and Michael I. Jordan. International Conference on Artificial Intelligence and Statistics (AISTATS), 2016. Arxiv (The Arxiv version adds an erratum for the AISTATS camera-ready version)

• Power of Ordered Hypothesis Testing, Lihua Lei, and William Fithian. International Conference on Machine Learning (ICML), 2016. Arxiv

Under Revision

• Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects, Wenlong Ji, Lihua Lei, Asher Spector, 2023. Under Major Revision Journal of Political Economy (JPE).

• Estimating the FDR of variable selection, Yixiang Luo, William Fithian, Lihua Lei, 2024. Under Major Revision at Annals of Statistics (AoS).

• Estimating Counterfactual Matrix Means with Short Panel Data, Lihua Lei and Brad Ross, 2023. Under R&R at Econometrica.

• Learning from a Biased Sample, Roshni Sahoo, Lihua Lei, and Stefan Wager, 2022. Under R&R at Management Science (MS).

Preprints

• Predictions as surrogates: Revisiting surrogate outcomes in the age of AI, Wenlong Ji, Lihua Lei, Tijana Zrnic, 2025

• Family-wise Error Rate Control with E-values, William Hartog, Lihua Lei, 2025

• Causal Interpretation of Regressions With Ranks, Lihua Lei, 2024

• Bellman Conformal Inference: Calibrating Prediction Intervals For Time Series, Zitong Yang, Emmanuel Candès, and Lihua Lei, 2024

• Inference for Synthetic Controls via Refined Placebo Tests, Lihua Lei and Timothy Sudijono, 2024

• Causal clustering: design of cluster experiments under network interference, Davide Viviano, Lihua Lei, Guido Imbens, Brian Karrer, Okke Schrijvers, Liang Shi, 2023

• Distribution-free inference on the extremum of conditional expectations via classification, Lihua Lei, 2023

• The Transfer Performance of Economics Models, Isaiah Andrews, Drew Fudenberg, Lihua Lei, Annie Liang, and Chaofeng Wu, 2023

• Policy Learning under Biased Sample Selection, Lihua Lei, Roshni Sahoo, and Stefan Wager, 2023.

• Distribution-Free Assessment of Population Overlap in Observational Studies, Lihua Lei, Alexander D'Amour, Peng Ding, Avi Feller, and Jasjeet Sekhon, 2021.

• BONuS: Multiple multivariate testing with a data-adaptivetest statistic, Chiao-Yu Yang, Lihua Lei, Nhat Ho, and William Fithian, 2021.

• Consistency of Spectral Clustering on Hierarchical Stochastic Block Models, Lihua Lei, Xiaodong Li, and Xingmei Lou, 2020.

• Unified Two-to-infinity Eigenspace Perturbation Theory for Symmetric Random Matrices, Lihua Lei, 2019.

Softwares

• adaptMT, an R package for AdaPT (adaptive p-value thresholding) on CRAN

  • A short demo

  • A long tutorial

• mkn, an R package for multiple-knockoffs based inference (an ongoing project)

• cfcausal, an R package for conformal inference of counterfactuals and individual treatment effects

• dbh, an R package for dependence-adjusted Benjamini-Hochberg and general step-up procedures

• transferUQ, an R package on uncertainty quantification of transfer performance for prediction models