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<title>Yuhao Wang | 王禹皓</title>
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<div class="menu-category">menu</div>
<div class="menu-item"><a href="index.html">Home</a></div>
<div class="current"><a href="publications.html" class="current">Publications</a></div>
<div class="menu-item"><a href="cv.pdf">CV</a></div>
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<h2>Preprints | 预印本</h2>
<ul>
<li><p><a href="https://arxiv.org/abs/2411.19789">Adjusting auxiliary variables under approximate neighborhood interference</a> <br/>
Xin Lu, <b>Y. Wang</b> and Zhiheng Zhang (α-β order) <br/>
Under review</p>
</li>
</ul>
<ul>
<li><p><a href="https://arxiv.org/abs/2411.11614">On the physics of nested Markov models: a generalized probabilistic theory perspective</a> <br/>
Xingjian Zhang and <b>Y. Wang</b></p>
</li>
</ul>
<ul>
<li><p><a href="https://arxiv.org/abs/2407.14778">Minimax estimation of functionals in sparse vector model with correlated observations</a> <br/>
<b>Y. Wang</b>, Pengkun Yang and Alexandre B. Tsybakov <br/>
Under review</p>
</li>
</ul>
<ul>
<li><p><a href="https://arxiv.org/abs/2312.02513">Asymptotic theory of the best-choice rerandomization using the Mahalanobis distance</a> <br/>
<b>Y. Wang</b> and Xinran Li <br/>
Minor revision at <i>Journal of Econometrics.</i></p>
</li>
</ul>
<ul>
<li><p><a href="https://arxiv.org/abs/2309.02073">Debiased regression adjustment in completely randomized experiments with moderately high-dimensional covariates</a> <br/>
Xin Lu, Fan Yang and <b>Y. Wang</b> <br/>
Minor revision at <i>The Annals of Statistics.</i></p>
</li>
</ul>
<ul>
<li><p><a href="https://arxiv.org/abs/2305.04174">Root-n consistent semiparametric learning with high-dimensional nuisance functions under minimal sparsity</a> <br/>
Lin Liu, Xinbo Wang and <b>Y. Wang</b> (α-β order)<br/>
Under review</p>
</li>
</ul>
<ul>
<li><p><a href="https://ssrn.com/abstract=3896229">Profit-driven experimental design</a> <br/>
<b>Y. Wang</b> and Weiming Zhu (α-β order)<br/>
Under review</p>
</li>
</ul>
<h2>Journal Publications | 期刊论文</h2>
<ul>
<li><p><a href="https://arxiv.org/abs/2211.16182">Residual permutation test for regression coefficient testing</a> <br/>
Kaiyue Wen*, Tengyao Wang*, and <b>Y. Wang</b> (*: equal contributions)<br/>
<i>The Annals of Statistics (accepted).</i></p>
</li>
</ul>
<ul>
<li><p><a href="https://doi.org/10.1093/jrsssb/qkae095">Long-term causal inference under persistent confounding via data combination</a> <br/>
Guido Imbens, Nathan Kallus, Xiaojie Mao and <b>Y. Wang</b> (α-β order)<br/>
<i>Journal of the Royal Statistical Society, Series B (accepted).</i></p>
</li>
</ul>
<ul>
<li><p><a href="http://dx.doi.org/10.1214/24-AOS2409">Debiased inverse propensity score weighting for estimation of average treatment effects with high-dimensional confounders</a> <br/>
<b>Y. Wang</b> and Rajen D. Shah <br/>
<i>The Annals of Statistics 52.5 (2024): 1978-2003.</i></p>
</li>
</ul>
<ul>
<li><p><a href="https://projecteuclid.org/journals/annals-of-statistics/volume-50/issue-6/Rerandomization-with-diminishing-covariate-imbalance-and-diverging-number-of-covariates/10.1214/22-AOS2235.short">Rerandomization with diminishing covariate imbalance and diverging number of covariates</a> <br/>
<b>Y. Wang</b> and Xinran Li <br/>
<i>The Annals of Statistics 50.6 (2022): 3439-3465.</i></p>
</li>
</ul>
<ul>
<li><p><a href="https://www.jmlr.org/papers/v23/20-1375.html">Joint inference of multiple graphs from matrix polynomials</a> <br/>
Madeline Navarro, <b>Y. Wang</b>, Antonio G. Marques, Caroline Uhler and Santiago Segarra<br/>
<i>Journal of Machine Learning Research 23.76 (2022): 1−35.</i> </p>
</li>
</ul>
<ul>
<li><p><a href="https://projecteuclid.org/journals/bernoulli/volume-28/issue-4/Estimation-of-the-math-overflowscroll-alttextell-_2-xmlnshttp/10.3150/21-BEJ1436.short">Estimation of the $\ell_2$-norm and testing in sparse linear regression with unknown variance</a> <br/>
Alexandra Carpentier, Olivier Collier, Laetitia Comminges, Alexandre B. Tsybakov and <b>Y. Wang</b> (α-β order)<br/>
<i>Bernoulli 28.4 (2022): 2744-2787.</i> </p>
</li>
</ul>
<ul>
<li><p><a href="https://academic.oup.com/biomet/article-abstract/108/4/795/6062392">Consistency guarantees for greedy permutation-based causal inference algorithms</a> <br/>
Liam Solus, <b>Y. Wang</b> and Caroline Uhler <br/>
<i>Biometrika 108.4 (2021): 795-814.</i></p>
</li>
</ul>
<ul>
<li><p><a href="https://projecteuclid.org/euclid.ejs/1593569024">High-dimensional joint estimation of multiple directed Gaussian graphical models</a> <br/>
<b>Y. Wang</b>, Santiago Segarra and Caroline Uhler <br/>
<i>Electronic Journal of Statistics 14.1 (2020): 2439-2483</i>.</p>
</li>
</ul>
<ul>
<li><p><a href="https://link.springer.com/article/10.1134/S0005117919100047">Minimax rate of testing in sparse linear regression</a> <br/>
Alexandra Carpentier, Olivier Collier, Laetitia Comminges, Alexandre B. Tsybakov and <b>Y. Wang</b> (α-β order)<br/>
<i>Automation and Remote Control 80.10 (2019): 1817-1834. (special issue in memory of Yakov Tsypkin)</i></p>
</li>
</ul>
<ul>
<li><p><a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0756-4">De novo ChIP-seq analysis</a> <br/>
Xin He*, A. Ercument Cicek*, <b>Y. Wang*</b>, Marcel H. Schulz, Hai-Son Le, and Ziv Bar-Joseph (*: equal contributions)<br/>
<i>Genome Biology 16.1 (2015): 205</i>.</p>
</li>
</ul>
<h2>Conference Proceedings | 会议论文</h2>
<ul>
<li><p><a href="http://proceedings.mlr.press/v124/squires20a.html">Permutation-based causal structure learning with unknown intervention targets</a> <br/>
Chandler Squires, <b>Y. Wang</b> and Caroline Uhler <br/>
<i>Uncertainty in Artificial Intelligence (UAI) 2020</i></p>
</ul>
<ul>
<li><p><a href="http://proceedings.mlr.press/v124/saeed20a.html">Anchored causal inference in the presence of measurement error</a> <br/>
Basil Saeed, Anastasiya Belyaeva, <b>Y. Wang</b> and Caroline Uhler <br/>
<i>Uncertainty in Artificial Intelligence (UAI) 2020</i></p>
</ul>
<ul>
<li><p><a href="http://proceedings.mlr.press/v108/wang20g.html">Learning high-dimensional Gaussian graphical models under total positivity without adjustment of tuning parameters</a> <br/>
<b>Y. Wang</b>, Uma Roy, Caroline Uhler <br/>
<i>International Conference on Artificial Intelligence and Statistics (AISTATS) 2020</i></p>
</li>
</ul>
<ul>
<li><p><a href="https://papers.nips.cc/paper/7634-direct-estimation-of-differences-in-causal-graphs">Direct estimation of differences in causal graphs</a> <br/>
<b>Y. Wang</b>, Chandler Squires, Anastasiya Belyaeva, Caroline Uhler <br/>
<i>Advances in Neural Information Processing Systems (NeurIPS) 2018</i></p>
</li>
</ul>
<ul>
<li><p><a href="http://papers.nips.cc/paper/7164-permutation-based-causal-inference-algorithms-with-interventions">Permutation-based causal inference algorithms with interventions</a> <br/>
<b>Y. Wang</b>, Liam Solus, Karren D. Yang, Caroline Uhler <br/>
<i>Advances in Neural Information Processing Systems (NeurIPS) 2017 <b>(accepted as spotlight presentation)</b></i>.</p>
</li>
</ul>
<ul>
<li><p><a href="https://ieeexplore.ieee.org/abstract/document/8335493">Joint inference of networks from stationary graph signals</a> <br/>
Santiago Segarra, <b>Y. Wang</b>, Caroline Uhler, Antonio G. Marques <br/>
<i>Asilomar Conference on Signals, Systems, and Computers 2017</i>.</p>
</li>
</ul>
<ul>
<li><p><a href="https://academic.oup.com/bioinformatics/article/32/12/i351/2240613">RCK: accurate and efficient inference of sequence and structure-based protein-RNA binding models from RNAcompete data</a> <br/>
Yaron Orenstein, <b>Y. Wang</b>, Bonnie Berger <br/>
<i>Intelligent Systems for Molecular Biology (ISMB) 2016</i>.</p>
</li>
</ul>
<ul>
<li><p><a href="https://academic.oup.com/bioinformatics/article-abstract/29/13/i126/197127">Predicting drug-target interactions using restricted Boltzmann machines</a> <br/>
<b>Y. Wang</b>, Jianyang Zeng <br/>
<i>Intelligent Systems for Molecular Biology (ISMB) 2013</i>.</p>
</li>
</ul>
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