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<!DOCTYPE html>
<html lang="en">
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<head> <!-- Metadata, OpenGraph and Schema.org -->
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<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
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<title>LINs Lab</title>
<meta name="author" content="LINs Lab at Westlake University (Since 2022.11)" />
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<meta name="keywords" content="westlake, deep learning, optimization, learning, inference, distributed deep learning, systems" />
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<h1 class="post-title">
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<source media="(max-width: 480px)" srcset="/assets/img/logo-480.webp"></source>
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</h1>
<p class="desc">
Welcome to the website of the <b><u>L</u></b>earning and <b><u>IN</u></b>ference <b><u>S</u></b>ystems (<b>LINs</b>) <b>Lab</b> at Westlake University!
</p>
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<article>
<div class="profile float-right">
</div>
<div class="clearfix">
<p>Our research interests lie in the intersection of optimization and generalization for deep learning:</p>
<ul>
<li>
<em>leveraging</em> theoretical/empirical understanding (e.g., loss landscape, and training dynamics)</li>
<li>
<em>to design</em> efficient & robust methods (both learning and inference)</li>
<li>
<em>for</em> deep learning (centralized) and collaborative deep learning (distributed and/or decentralized),</li>
<li>
<em>under</em> imperfect environments (e.g., noisy, heterogeneous, and hardware-constrained).</li>
</ul>
<p>Lab activities:</p>
<ul>
<li>We are running a research seminar on <em><a href="https://dlo-seminar.github.io/" target="_blank" rel="noopener noreferrer">Deep Learning and Optimization</a></em>.</li>
</ul>
</div>
<!-- News -->
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<br>
<h2>News</h2>
<div class="table-responsive">
<table class="table table-sm table-borderless">
<tr>
<th scope="row">Sep 27, 2024</th>
<td>
Two papers of our group were accepted at this year’s <a href="https://neurips.cc/" target="_blank" rel="noopener noreferrer">NeurIPS 2024</a> conference.
Congratulations to Peng on the acceptance of his paper on <a href="https://arxiv.org/abs/2405.14669" target="_blank" rel="noopener noreferrer">Ideal Data</a>, and congratulations to Jiamin for his attempts on <a href="https://arxiv.org/abs/2306.01176" target="_blank" rel="noopener noreferrer">Cooperative Hardware-Prompt Learning</a>.
</td>
</tr>
<tr>
<th scope="row">Jul 2, 2024</th>
<td>
Congratulations to Yuxuan on the oral acceptance of his paper on <a href="https://arxiv.org/abs/2401.16355" target="_blank" rel="noopener noreferrer">multimodal benchmark in pathology</a> at <a href="https://eccv2024.ecva.net/" target="_blank" rel="noopener noreferrer">ECCV 2024</a>.
<strong>This paper was selected as the one of best paper candidates at ECCV.</strong>
</td>
</tr>
<tr>
<th scope="row">May 2, 2024</th>
<td>
Our paper on <a href="https://arxiv.org/abs/2301.12379" target="_blank" rel="noopener noreferrer">distribution-shift-robust Federated Learning</a> was accepted at this year’s <a href="https://icml.cc/" target="_blank" rel="noopener noreferrer">ICML 2024</a> conference. Congratulations to Yongxin.
</td>
</tr>
<tr>
<th scope="row">Feb 27, 2024</th>
<td>
Our paper on <a href="https://arxiv.org/abs/2312.03526" target="_blank" rel="noopener noreferrer">efficient dataset distillation</a> was accepted at this year’s <a href="https://cvpr.thecvf.com/" target="_blank" rel="noopener noreferrer">CVPR 2024</a> conference.
Congratulations to Peng (he also received a CVPR 2024 travel grant).
</td>
</tr>
<tr>
<th scope="row">Feb 4, 2024</th>
<td>
We are looking for self-motivated research internship students! Please email us with your CV.
</td>
</tr>
</table>
</div>
</div>
<!-- Selected papers -->
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<h2>Selected publications</h2>
<ol class="bibliography">
<li>
<!-- _layouts/bib.html -->
<div class="row">
<div class="col-sm-2 abbr"><abbr class="badge">NeurIPS 2024</abbr></div>
<!-- Entry bib key -->
<div id="sun2024efficiency" class="col-sm-8">
<!-- Title -->
<div class="title">Efficiency for Free: Ideal Data Are Transportable Representations</div>
<!-- Author -->
<div class="author">
<a href="https://sp12138.github.io/" target="_blank" rel="noopener noreferrer">Sun, Peng</a>, Jiang, Yi, and <a href="https://tlin-taolin.github.io/" target="_blank" rel="noopener noreferrer">Lin, Tao</a>
</div>
<!-- Journal/Book title and date -->
<div class="periodical">
<em>In Advances in Neural Information Processing Systems (NeurIPS)</em> 2024
</div>
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<div class="links">
<a href="https://arxiv.org/abs/2405.14669" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
<a href="https://github.com/LINs-lab/ReLA" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">Code</a>
</div>
</div>
</div>
</li>
<li>
<!-- _layouts/bib.html -->
<div class="row">
<div class="col-sm-2 abbr"><abbr class="badge">ICLR 2023</abbr></div>
<!-- Entry bib key -->
<div id="jiang2023test" class="col-sm-8">
<!-- Title -->
<div class="title">Test-Time Robust Personalization for Federated Learning</div>
<!-- Author -->
<div class="author">
<a href="https://liangzejiang.github.io/" target="_blank" rel="noopener noreferrer">Jiang, Liangze*</a>, and <a href="https://tlin-taolin.github.io/" target="_blank" rel="noopener noreferrer">Lin, Tao*†</a>
</div>
<!-- Journal/Book title and date -->
<div class="periodical">
<em>In International Conference on Learning Representations (ICLR)</em> 2023
</div>
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<div class="links">
<a href="https://arxiv.org/abs/2205.10920" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
<a href="https://github.com/lins-lab/fedthe" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">Code</a>
</div>
</div>
</div>
</li>
<li>
<!-- _layouts/bib.html -->
<div class="row">
<div class="col-sm-2 abbr"><abbr class="badge">ICML 2021</abbr></div>
<!-- Entry bib key -->
<div id="lin2021quasi" class="col-sm-8">
<!-- Title -->
<div class="title">Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data</div>
<!-- Author -->
<div class="author">
<a href="https://tlin-taolin.github.io/" target="_blank" rel="noopener noreferrer">Lin, Tao</a>, <a href="https://spkreddy.org/" target="_blank" rel="noopener noreferrer">Karimireddy, Sai Praneeth</a>, <a href="https://www.sstich.ch/" target="_blank" rel="noopener noreferrer">Stich, Sebastian U.</a>, and <a href="https://scholar.google.com/citations?user=r1TJBr8AAAAJ&hl=en" target="_blank" rel="noopener noreferrer">Jaggi, Martin</a>
</div>
<!-- Journal/Book title and date -->
<div class="periodical">
<em>In International Conference on Machine Learning (ICML)</em> 2021
</div>
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<div class="links">
<a href="https://arxiv.org/abs/2102.04761" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
<a href="https://github.com/epfml/quasi-global-momentum" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">Code</a>
</div>
</div>
</div>
</li>
<li>
<!-- _layouts/bib.html -->
<div class="row">
<div class="col-sm-2 abbr"><abbr class="badge">ICLR 2020</abbr></div>
<!-- Entry bib key -->
<div id="koloskova2020decentralized" class="col-sm-8">
<!-- Title -->
<div class="title">Decentralized Deep Learning with Arbitrary Communication Compression</div>
<!-- Author -->
<div class="author">
<a href="https://koloskova.github.io/" target="_blank" rel="noopener noreferrer">Koloskova, Anastasia*</a>, <a href="https://tlin-taolin.github.io/" target="_blank" rel="noopener noreferrer">Lin, Tao*</a>, <a href="https://www.sstich.ch/" target="_blank" rel="noopener noreferrer">Stich, Sebastian U</a>, and <a href="https://scholar.google.com/citations?user=r1TJBr8AAAAJ&hl=en" target="_blank" rel="noopener noreferrer">Jaggi, Martin</a>
</div>
<!-- Journal/Book title and date -->
<div class="periodical">
<em>In International Conference on Learning Representations (ICLR)</em> 2020
</div>
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<div class="links">
<a href="https://arxiv.org/abs/1907.09356" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
<a href="https://github.com/epfml/ChocoSGD" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">Code</a>
</div>
</div>
</div>
</li>
<li>
<!-- _layouts/bib.html -->
<div class="row">
<div class="col-sm-2 abbr"><abbr class="badge">ICLR 2020</abbr></div>
<!-- Entry bib key -->
<div id="lin2020dont" class="col-sm-8">
<!-- Title -->
<div class="title">Don’t Use Large Mini-batches, Use Local SGD</div>
<!-- Author -->
<div class="author">
<a href="https://tlin-taolin.github.io/" target="_blank" rel="noopener noreferrer">Lin, Tao</a>, <a href="https://www.sstich.ch/" target="_blank" rel="noopener noreferrer">Stich, Sebastian U</a>, <a href="https://kishinmh.github.io/" target="_blank" rel="noopener noreferrer">Patel, Kumar Kshitij</a>, and <a href="https://scholar.google.com/citations?user=r1TJBr8AAAAJ&hl=en" target="_blank" rel="noopener noreferrer">Jaggi, Martin</a>
</div>
<!-- Journal/Book title and date -->
<div class="periodical">
<em>In International Conference on Learning Representations (ICLR)</em> 2020
</div>
<!-- Links/Buttons -->
<div class="links">
<a href="https://arxiv.org/abs/1808.07217" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
<a href="https://github.com/epfml/LocalSGD-Code" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">Code</a>
</div>
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</li>
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© Copyright 2024 LINs Lab at Westlake University (Since 2022.11). Last updated: November 25, 2024.
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