Yanglei Song's website

My research focuses on sequential decision-making, including multiple testing, change-point detection, adaptive experimental design, and contextual bandits. I also work on statistical inference in complex settings, such as high-dimensional inference, U-statistics, regression with thresholded covariates, and inference under covariate-adaptive designs.

Education:

Ph.D. in Statistics, University of Illinois at Urbana-Champaign, 2019

M.Sc. in Mathematics, University of Illinois at Urbana-Champaign, 2016

B. Eng. in Electronic Engineering, Tsinghua University, 2012

Publication and preprints:

* indicates supervised students;  (a) indicates alphabetical order.

• J. Liu*, and Y. Song, "Minimax Rate-Optimal Algorithms for High-Dimensional Stochastic Linear Bandits", arXiv:2505.17400

• Y. Song, B. Dulek, and S. Gezici, "Optimal Decision Rules for Composite Binary Hypothesis Testing under Neyman-Pearson Framework", arXiv:2505.17851

• Y. Zhou*, Y. Song, and S. Yüksel, "Robustness to Model Approximation, Model Learning From Data, and Sample Complexity in Wasserstein Regular MDPs", arXiv:2410.14116.

• Z. Wang*, Y. Song, W. Jiang, and D. Tu, "Nonparametric Inference on Treatment-Biomarker Interaction Based on Probability Index", submitted.

• (a) Y. Ke, H. Ling, and Y. Song, "Distance Correlation in Multiple Biased Sampling Models", arXiv:2408.11808

• Y. Song and G. Fellouris, "Change acceleration and detection'',  Annals of Statistics 2024, Vol. 52, No. 3, 1050-1075.

• Z. Zhao*, Y. Song, W. Jiang, and D. Tu, "Consistent covariances estimation for stratum imbalances under marginal design for covariate adaptive randomization", Scandinavian Journal of Statistics, 2024, 1-30 (https://doi.org/10.1111/sjos.12703).

• Y. Song and M. Zhou*, "Truncated LinUCB for stochastic linear bandits",  arXiv:2202.11735. (code)

• N. Li*, Y. Song, D. Lin,  and D. Tu, "Bootstrap adjusted predictive classification under generalized linear models",  Electronic Journal of Statistics 2023, Vol. 17, No. 1, 548-606.

• Y. Song, X. Chen, and K. Kato, "Stratified incomplete local simplex tests for curvature of nonparametric multiple regression." Bernoulli 29 (1) 323 - 349, February 2023. https://doi.org/10.3150/22-BEJ1459 . (code)

• N. Li*, Y. Song, D. Lin, and D. Tu, "Bootstrap adjustment to minimum p-value method for predictive classification",  Statistica Sinica 33 (2023), 2065-2086.  (supplement file),

• Y. Song, X. Chen, and K. Kato,  "Approximating high-dimensional infinite-order U-statistics: Statistical and computational guarantees", Electronic Journal of Statistics 13.2 (2019): 4794-4848. 

• Y. Song and G. Fellouris, "Sequential multiple testing with generalized error control: an asymptotic optimality theory'', Annals of Statistics 2019, Vol. 47, No. 3, 1776-1803.

• Y. Song and G. Fellouris, "Asymptotically optimal, sequential, multiple testing procedures with prior information on the number of signals'', Electronic Journal of Statistics, vol. 11, no. 1, (2017) 338--363.

• Y. Song and G. Fellouris, "Logarithmically efficient simulation for misclassification probabilities in sequential multiple testing'', Winter Simulation Conference (WSC'16), Arlington, Virginia, Dec 2016.

• C. Wang, Y. Sun, Y. Song, J. Han, and Y. Song, "Querying similar relations in schema-rich heterogeneous information networks", in Proc of 2016 SIAM Int. Conf. on Data Mining (SDM'16), Miami, FL, May 2016.

• C. Wang, X. Liu, Y. Song, and J. Han,  "Towards interactive construction of topical hierarchy: a recursive tensor decomposition approach'', in Proc. of 2015 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'15), Sydney, Australia, Aug. 2015.

• A. El-Kishky, Y. Song, C. Wang, C. R. Voss, and J. Han, "Scalable topical phrase mining from text corpora'', PVLDB 8(3): 305 - 316, 2015. (Also, in Proc. 2015 Int. Conf. on Very Large Data Bases (VLDB'15), Kohala Coast, Hawaii, Sept. 2015.)

• C. Wang, X. Liu, Y. Song, and J. Han,  "Scalable moment-based inference for latent Dirichlet allocation'', in Proc. of 2014 European Conf. on Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECMLPKDD'14), Nancy, France, Sept. 2014.