Preprints | 预印本

• Asymptotic theory of the best-choice rerandomization using the Mahalanobis distance
  Y. Wang and Xinran Li

• Debiased regression adjustment in completely randomized experiments with moderately high-dimensional covariates
  Xin Lu, Fan Yang, and Y. Wang

• Root-n consistent semiparametric learning with high-dimensional nuisance functions under minimal sparsity
  Lin Liu*, and Y. Wang*

• Residual permutation test for high-dimensional regression coefficient testing
  Kaiyue Wen*, Tengyao Wang*, and Y. Wang
  Major revision at The Annals of Statistics.

• Long-term causal inference under persistent confounding via data combination
  Guido Imbens*, Nathan Kallus*, Xiaojie Mao* and Y. Wang*
  Major revision at Journal of the Royal Statistical Society: Series B.

• Profit-driven experimental design
  Y. Wang* and Weiming Zhu*

• Debiased inverse propensity score weighting for estimation of average treatment effects with high-dimensional confounders
  Y. Wang and Rajen D. Shah
  Major revision at The Annals of Statistics.

Journal Publications | 期刊论文

• Rerandomization with diminishing covariate imbalance and diverging number of covariates
  Y. Wang and Xinran Li
  The Annals of Statistics 50.6 (2022): 3439-3465.

• Joint inference of multiple graphs from matrix polynomials
  Madeline Navarro, Y. Wang, Antonio G. Marques, Caroline Uhler and Santiago Segarra
  Journal of Machine Learning Research 23.76 (2022): 1−35.

• Estimation of the $\ell_2$-norm and testing in sparse linear regression with unknown variance
  Alexandra Carpentier*, Olivier Collier*, Laetitia Comminges*, Alexandre B. Tsybakov* and Y. Wang*
  Bernoulli 28.4 (2022): 2744-2787.

• Consistency guarantees for greedy permutation-based causal inference algorithms
  Liam Solus, Y. Wang and Caroline Uhler
  Biometrika 108.4 (2021): 795-814.

• High-dimensional joint estimation of multiple directed Gaussian graphical models
  Y. Wang, Santiago Segarra and Caroline Uhler
  Electronic Journal of Statistics 14.1 (2020): 2439-2483.

• Minimax rate of testing in sparse linear regression
  Alexandra Carpentier*, Olivier Collier*, Laetitia Comminges*, Alexandre B. Tsybakov* and Y. Wang*
  Automation and Remote Control 80.10 (2019): 1817-1834. (special issue in memory of Yakov Tsypkin)

• De novo ChIP-seq analysis
  Xin He*, A. Ercument Cicek*, Y. Wang*, Marcel H. Schulz, Hai-Son Le, and Ziv Bar-Joseph
  Genome Biology 16.1 (2015): 205.

Conference Proceedings | 会议论文

• Permutation-based causal structure learning with unknown intervention targets
  Chandler Squires, Y. Wang and Caroline Uhler
  Uncertainty in Artificial Intelligence (UAI) 2020

• Anchored causal inference in the presence of measurement error
  Basil Saeed, Anastasiya Belyaeva, Y. Wang and Caroline Uhler
  Uncertainty in Artificial Intelligence (UAI) 2020

• Learning high-dimensional Gaussian graphical models under total positivity without adjustment of tuning parameters
  Y. Wang, Uma Roy, Caroline Uhler
  International Conference on Artificial Intelligence and Statistics (AISTATS) 2020

• Direct estimation of differences in causal graphs
  Y. Wang, Chandler Squires, Anastasiya Belyaeva, Caroline Uhler
  Advances in Neural Information Processing Systems (NeurIPS) 2018

• Permutation-based causal inference algorithms with interventions
  Y. Wang, Liam Solus, Karren D. Yang, Caroline Uhler
  Advances in Neural Information Processing Systems (NeurIPS) 2017 (accepted as spotlight presentation).

• Joint inference of networks from stationary graph signals
  Santiago Segarra, Y. Wang, Caroline Uhler, Antonio G. Marques
  Asilomar Conference on Signals, Systems, and Computers 2017.

• RCK: accurate and efficient inference of sequence and structure-based protein-RNA binding models from RNAcompete data
  Yaron Orenstein, Y. Wang, Bonnie Berger
  Intelligent Systems for Molecular Biology (ISMB) 2016.

• Predicting drug-target interactions using restricted Boltzmann machines
  Y. Wang, Jianyang Zeng
  Intelligent Systems for Molecular Biology (ISMB) 2013.