| Predicting drug-target interactions using restricted Boltzmann machines Y Wang, J Zeng Bioinformatics 29 (13), i126-i134, 2013 | 261 | 2013 |
| Permutation-based causal inference algorithms with interventions Y Wang, L Solus, K Yang, C Uhler Advances in Neural Information Processing Systems 30, 2017 | 181 | 2017 |
| Consistency guarantees for greedy permutation-based causal inference algorithms L Solus, Y Wang, C Uhler Biometrika 108 (4), 795-814, 2021 | 152 | 2021 |
| Permutation-based causal structure learning with unknown intervention targets C Squires, Y Wang, C Uhler Conference on Uncertainty in Artificial Intelligence, 1039-1048, 2020 | 129 | 2020 |
| RCK: accurate and efficient inference of sequence-and structure-based protein–RNA binding models from RNAcompete data Y Orenstein, Y Wang, B Berger Bioinformatics 32 (12), i351-i359, 2016 | 90 | 2016 |
| Long-term causal inference under persistent confounding via data combination G Imbens, N Kallus, X Mao, Y Wang Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2025 | 69 | 2025 |
| Direct estimation of differences in causal graphs Y Wang, C Squires, A Belyaeva, C Uhler Advances in neural information processing systems 31, 2018 | 50 | 2018 |
| Joint inference of multiple graphs from matrix polynomials M Navarro, Y Wang, AG Marques, C Uhler, S Segarra Journal of machine learning research 23 (76), 1-35, 2022 | 41 | 2022 |
| High-dimensional joint estimation of multiple directed Gaussian graphical models Y Wang, S Segarra, C Uhler Electronic Journal of Statistics 14 (1), 2439-2483, 2020 | 32 | 2020 |
| Minimax rate of testing in sparse linear regression A Carpentier, O Collier, L Comminges, AB Tsybakov, Y Wang Automation and Remote Control 80 (10), 1817-1834, 2019 | 31 | 2019 |
| Debiased inverse propensity score weighting for estimation of average treatment effects with high-dimensional confounders Y Wang, RD Shah The Annals of Statistics 52 (5), 1978-2003, 2024 | 29* | 2024 |
| Rerandomization with diminishing covariate imbalance and diverging number of covariates Y Wang, X Li The Annals of Statistics 50 (6), 3439-3465, 2022 | 29 | 2022 |
| Learning High-dimensional Gaussian Graphical Models under Total Positivity without Adjustment of Tuning Parameters Y Wang, U Roy, C Uhler International Conference on Artificial Intelligence and Statistics, 2698-2708, 2020 | 27 | 2020 |
| Joint inference of networks from stationary graph signals S Segarra, Y Wang, C Uhler, AG Marques 2017 51st Asilomar Conference on Signals, Systems, and Computers, 975-979, 2017 | 24 | 2017 |
| Anchored Causal Inference in the Presence of Measurement Error B Saeed, A Belyaeva, Y Wang, C Uhler Conference on Uncertainty in Artificial Intelligence, 619-628, 2020 | 22 | 2020 |
| De novo ChIP-seq analysis X He, AE Cicek, Y Wang, MH Schulz, HS Le, Z Bar-Joseph Genome biology 16 (1), 205, 2015 | 18 | 2015 |
| Debiased regression adjustment in completely randomized experiments with moderately high-dimensional covariates X Lu, F Yang, Y Wang The Annals of Statistics 53 (4), 1535-1558, 2025 | 14 | 2025 |
| Residual permutation test for regression coefficient testing K Wen, T Wang, Y Wang The Annals of Statistics 53 (2), 724-748, 2025 | 13* | 2025 |
| Estimation of the ℓ2-norm and testing in sparse linear regression with unknown variance A Carpentier, O Collier, L Comminges, AB Tsybakov, Y Wang Bernoulli 28 (4), 2744-2787, 2022 | 12 | 2022 |
| Asymptotic theory of the best-choice rerandomization using the Mahalanobis distance Y Wang, X Li Journal of Econometrics 251, 106049, 2025 | 7 | 2025 |
Caroline UhlerMassachusetts Institute of TechnologyVerified email at mit.edu
