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Yuhao Wang | 王禹皓
Yuhao Wang | 王禹皓
Institute for Interdisciplinary Information Sciences, Tsinghua University
Подтвержден адрес электронной почты в домене tsinghua.edu.cn - Главная страница
Название
Процитировано
Процитировано
Год
Predicting drug-target interactions using restricted Boltzmann machines
Y Wang, J Zeng
Bioinformatics 29 (13), i126-i134, 2013
2472013
Permutation-based causal inference algorithms with interventions
Y Wang, L Solus, K Yang, C Uhler
Advances in Neural Information Processing Systems 30, 2017
1492017
Consistency guarantees for greedy permutation-based causal inference algorithms
L Solus, Y Wang, C Uhler
Biometrika 108 (4), 795-814, 2021
1222021
Permutation-based causal structure learning with unknown intervention targets
C Squires, Y Wang, C Uhler
Conference on Uncertainty in Artificial Intelligence, 1039-1048, 2020
932020
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
802016
Direct estimation of differences in causal graphs
Y Wang, C Squires, A Belyaeva, C Uhler
Advances in neural information processing systems 31, 2018
412018
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 …, 2024
392024
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
352022
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
302020
Minimax rate of testing in sparse linear regression
A Carpentier, O Collier, L Comminges, AB Tsybakov, Y Wang
Automation and Remote Control 80, 1817-1834, 2019
302019
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
282020
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
252017
Rerandomization with diminishing covariate imbalance and diverging number of covariates
Y Wang, X Li
The Annals of Statistics 50 (6), 3439-3465, 2022
212022
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
19*2024
De novo ChIP-seq analysis
X He, AE Cicek, Y Wang, MH Schulz, HS Le, Z Bar-Joseph
Genome biology 16, 1-10, 2015
182015
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
152020
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
92022
Residual permutation test for regression coefficient testing
K Wen, T Wang, Y Wang
Annals of Statistics, 2024
7*2024
Debiased Regression Adjustment in Completely Randomized Experiments with Moderately High-dimensional Covariates
X Lu, F Yang, Y Wang
arXiv preprint arXiv:2309.02073, 2023
62023
Root-n consistent semiparametric learning with high-dimensional nuisance functions under minimal sparsity
L Liu, X Wang, Y Wang
arXiv preprint arXiv:2305.04174, 2023
22023
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