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publications.html
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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.1//EN"
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<table summary="Table for page layout." id="tlayout">
<tr valign="top">
<td id="layout-menu">
<div class="menu-category">Wenjun Zhao</div>
<div class="menu-item"><a href="index.html">About</a></div>
<div class="menu-item"><a href="CV_Zhao.pdf">CV</a></div>
<div class="menu-category">Teaching</div>
<div class="menu-item"><a href="teaching_current.html">Upcoming</a></div>
<div class="menu-item"><a href="teaching_past.html">Past</a></div>
<div class="menu-category">Research</div>
<div class="menu-item"><a href="publications.html" class="current">Publications</a></div>
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<p><br />
</p>
<h1>Publication list</h1>
<p>Authors are listed alphabetically, unless not.
</p>
<ol>
<li><p>EG Tabak, G Trigila, and W Zhao. <br />
<b>The Hierarchical Barycenter: Conditional Probability Simulation with Structured and Unobserved Covariates</b> <br />
<i>submitted to Machine Learning</i>
</p>
</li>
<li><p>W Zhao, E Larschan, B Sandstede, and R Singh. <br />
<b>Optimal transport reveals dynamic gene regulatory networks via gene velocity estimation</b> <a href="https://www.biorxiv.org/content/10.1101/2024.09.12.612590v1" target=“blank”>(preprint)</a> <br />
<i>submitted to PLOS Computational Biology</i>
</p>
</li>
<li><p>W Zhao, S Maffa, and B Sandstede. <br />
<b>Data-driven Continuation of Patterns and their Bifurcations</b> <a href="poster_dynamicsdays.pdf" target=“blank”>(poster)</a> <a href="http://www.bjornsandstede.com/papers/Data_Driven_Continuation.pdf" target=“blank”>(preprint)</a><br />
<i>in revision at SIAM Journal on Applied Dynamical Systems</i>.
</p>
</li>
<li><p>W Zhao and EG Tabak. <br />
<b>Adaptive Kernel Conditional Density Estimation</b> <a href="https://math.nyu.edu/~tabak/publications/KCDE_Paper.pdf" target=“blank”>(preprint)</a> <br />
<i>accepted at Information and Inference: A Journal of the IMA</i>.
</p>
</li>
<li><p>EG Tabak, G Trigila, and W Zhao. <br />
<b>The Conditional Barycenter Problem, its Data-Driven Formulation and its Solution through Normalizing Flows</b> <a href="https://www.intlpress.com/site/pub/pages/journals/items/cms/content/vols/0022/0006/a008/" target=“blank”>(publication)</a> <br />
<i>Communications in Mathematical Sciences (2024)</i>.
</p>
</li>
<li><p>H Zhang and W Zhao. <br />
<b>A Memory-efficient Neural ODE Framework Based on High-level Adjoint Differentiation</b> <a href="https://arxiv.org/abs/2206.01298" target=“blank”>(preprint)</a> <a href="https://ieeexplore.ieee.org/document/9999163" target=“blank”>(publication)</a> <br />
<i>IEEE Transactions on Artificial Intelligence (2022)</i>.
</p>
</li>
<li><p>EG Tabak, G Trigila, and W Zhao. <br />
<b>Distributional barycenter problem through data-driven flows</b> <a href="https://arxiv.org/abs/2104.14329" target=“blank”>(preprint)</a> <a href="https://www.sciencedirect.com/science/article/pii/S003132032200276X" target=“blank”>(publication)</a><br />
<i>Pattern Recognition (2022)</i>.
</p>
</li>
<li><p>W Zhao. <br />
<b>Sample-based Optimal Transport in Statistical Data analysis</b>.<br />
<i>PhD thesis</i>
</p>
</li>
<li><p>EG Tabak, G Trigila, and W Zhao. <br />
<b>Conditional density estimation and simulation through optimal transport</b> <a href="https://math.nyu.edu/~tabak/publications/Tabak_Trigila_Zhao.pdf" target=“blank”>(preprint)</a><a href="https://link.springer.com/article/10.1007/s10994-019-05866-3" target=“blank”>(publication)</a><br />
<i>Machine Learning (2020)</i>.
</p>
</li>
<li><p>EG Tabak, G Trigila, and W Zhao. <br />
<b>Data Driven Conditional Optimal Transport</b> <a href="poster_COT.pdf" target=“blank”>(poster)</a> <a href="https://math.nyu.edu/~tabak/publications/Tabak_Trigila_Zhao_COT.pdf" target=“blank”>(preprint)</a> <a href="https://link.springer.com/article/10.1007/s10994-021-06060-0" target=“blank”>(publication)</a><br />
<i>33rd Conference on Neural Information Processing Systems (NeurIPS) OTML Workshop (2019)</i>.
(Extended version in <i>Machine Learning (2021)</i>)
</p>
</li>
</ol>
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