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<!DOCTYPE html>
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<meta name="author" content="Han Bao">
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<h1>
<a href="./index.html">Hui Li</a>
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<a class="link" href="./publication/index.html">Publication</a>
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<h3 id="about-me">About me</h3>
<img id="profile-image" src="./file/profile.jpg" width=150px />
<div id="profile">
<li class="nostyle icon-misc">Master student in <a href="https://english.nudt.edu.cn/">National University of Defense Technology (NUDT)</a></li>
<li class="nostyle icon-people">Member of National Key Laboratory of Science and Technology on ATR</li>
<li class="nostyle icon-mail">[email protected]</li>
<li class="nostyle icon-file"><a href="/file/cv.pdf">CV</a>, <a href="https://github.com/LiHui-ML/">GitHub</a></li>
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<hr>
<h3 id="research-interests">Research interests</h3>
<p>My current focus of research is in learning from noisy distribution, particularly I tries to introduce label-noise learning to Space Situation Awareness.</p>
<ol>
<li>Label-noise learning. Utilizing instance with noisy labels to train networks.</li>
<li>Space debris detection, vital in Space Situation Awareness, requiring detection small and dim debris from observation. Noisy labels widely exist, thus label-noise learning is adopted</li>
</ol>
<!-- <p>I am glad to have discussions with those who have common interests!
You may have a look at the slides of my past talks such as <a href="/slides/202007_KyotoU.pdf">this</a> to see my tastes.</p> -->
<hr>
<h3 id="news">News</h3>
<ul>
<li>Sep 10, 2022: Our paper “Co-correcting: Combat Noisy Labels in Space Debris Detection” has been accepted by Remote Sensing. We introduce label-noise learning in space debris detection, alleviating the effect of noisy labels</a>.</li>
<li>Jan. 14, 2022: Our paper <a href="https://ieeexplore.ieee.org/abstract/document/9712750">“Combat Noisy Labels by Joint Training”</a> has been accepted in IEEE ICCECE.</li>
<li>July 1, 2020: I am the 1st year Master student in National University of Defense Technology (NUDT).</li>
</ul>
<details>
<summary>Show more</summary>
<ul>
<!-- <li>Jan 19, 2022: Our paper “Pairwise Supervision Can Provably Elicit a Decision Boundary” has been accepted by AISTATS2022. We elucidated that pairwise supervision (i.e., information indicating whether two input vectors belong to the same underlying class) is sufficient to recover a binary decision boundary. The latest version is available <a href="https://arxiv.org/abs/2006.06207">here</a> (updated on Mar 3).</li>
<li>Jun 21, 2021: Our paper <a href="https://arxiv.org/abs/2002.00995">“Learning from Noisy Similar and Dissimilar Data”</a> has been accepted by ECMLPKDD2021.</li>
<li>May 17, 2021: We have publicized a <a href="https://arxiv.org/abs/2005.13748">corrigendum</a> to our COLT2020 paper. The definition of calibrated losses is corrected and the proofs of our main results are modified.</li>
<li>Jan 23, 2021: Our paper <a href="http://proceedings.mlr.press/v130/bao21b.html">“Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation”</a> has been accepted by AISTATS2021!</li>
<li>Jan 8, 2021: Our presentation at IBIS2020 got the best presentation award (1st place out of 116 presentations)!</li> -->
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<span>© Hui Li / Last updated: 2022-09-16</span>
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