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    <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!
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      <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 &amp; 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>

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            <br>
            <h2>News</h2>
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                <tr>
                  <th scope="row">Feb 27, 2025</th>
                  <td>
                    Our paper on <a href="https://arxiv.org/abs/2412.12077" target="_blank" rel="noopener noreferrer">Unified Multimodal Foundation Model for Computational Pathology</a> was accepted at this year’s <a href="https://cvpr.thecvf.com/" target="_blank" rel="noopener noreferrer">CVPR 2025</a> conference. Congratulations to Yuxuan.
 
                  </td>
                </tr> 
                <tr>
                  <th scope="row">Jan 22, 2025</th>
                  <td>
                    Several papers from our group were accepted at this year’s <a href="https://iclr.cc/" target="_blank" rel="noopener noreferrer">ICLR 2025</a> conference. 
Congratulations to our PhD student <a href="https://gyxxyg.github.io/yongxinguo/" target="_blank" rel="noopener noreferrer">Yongxin GUO</a> on his three first-author acceptance, and to our PhD student Futing WANG on her first first-author acceptance. 
We also extend the same congratulations to our PhD students <a href="https://zhenglin-cheng.com/" target="_blank" rel="noopener noreferrer">Zhenglin CHENG</a>, <a href="https://sp12138.github.io/" target="_blank" rel="noopener noreferrer">Peng SUN</a>, and <a href="https://scholar.google.com/citations?user=bKh1cAoAAAAJ&amp;hl=zh-CN" target="_blank" rel="noopener noreferrer">Yuxuan SUN</a>, and to our internship students <a href="https://shangxinyi.github.io/" target="_blank" rel="noopener noreferrer">Xinyi SHANG</a>, Jiamu ZHENG, and <a href="https://scholar.google.com/citations?user=idZOK_kAAAAJ&amp;hl=en" target="_blank" rel="noopener noreferrer">Jinwei YAO</a>.

<ul>
  <li>
<strong>Oral</strong> (1.8%) -&gt; <a href="https://openreview.net/forum?id=rFpZnn11gj" target="_blank" rel="noopener noreferrer">PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration</a>
</li>
  <li>
<strong>Spotlight</strong> (5.1%) -&gt; <a href="https://openreview.net/forum?id=2c7pfOqu9k" target="_blank" rel="noopener noreferrer">DeFT: Decoding with Flash Tree-attention for Efficient Tree-structured LLM Inference</a>
</li>
  <li><a href="https://openreview.net/forum?id=CI4sCBMXjP" target="_blank" rel="noopener noreferrer">ELICIT: LLM Augmentation Via External In-context Capability</a></li>
  <li><a href="https://openreview.net/forum?id=T26f9z2rEe" target="_blank" rel="noopener noreferrer">Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models</a></li>
  <li><a href="https://openreview.net/forum?id=FoF5RaA3ug" target="_blank" rel="noopener noreferrer">GIFT: Unlocking Full Potential of Labels in Distilled Dataset at Near-zero Cost</a></li>
  <li><a href="https://openreview.net/forum?id=zPDpdk3V8L" target="_blank" rel="noopener noreferrer">Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies</a></li>
  <li><a href="https://openreview.net/forum?id=2PzozgigiA" target="_blank" rel="noopener noreferrer">CollabEdit: Towards Non-destructive Collaborative Knowledge Editing</a></li>
</ul>
 
                  </td>
                </tr> 
                <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>
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            <h2>Selected publications</h2>
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          <div class="title">UCGM: Unified Continuous Generative Models</div>
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<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>
                  
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            <em>(arXiv:2505.07447)</em> preprint
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        <div class="col-sm-2 abbr"><abbr class="badge">ICLR 2025</abbr></div>

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          <div class="title">DeFT: Decoding with Flash Tree-Attention for Efficient Tree-structured LLM Inference</div>
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<a href="https://scholar.google.com/citations?user=idZOK_kAAAAJ&amp;hl=en" target="_blank" rel="noopener noreferrer">Yao, Jinwei*</a>, Chen, Kaiqi*, <a href="https://zkx06111.github.io/" target="_blank" rel="noopener noreferrer">Zhang, Kexun</a>, <a href="https://cs.stanford.edu/~jiaxuan/" target="_blank" rel="noopener noreferrer">You, Jiaxuan</a>, <a href="https://binhangyuan.github.io/site/" target="_blank" rel="noopener noreferrer">Yuan, Binhang</a>, <a href="https://wangzeke.github.io/" target="_blank" rel="noopener noreferrer">Wang, Zeke</a>, and <a href="https://tlin-taolin.github.io/" target="_blank" rel="noopener noreferrer">Lin, Tao</a>
                  
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            <em>In International Conference on Learning Representations (ICLR), Spotlight, abridged in ICLR workshop AGI (Oral),</em> 2025
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            <a href="https://github.com/LINs-lab/DeFT" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">Code</a>
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        <div class="col-sm-2 abbr"><abbr class="badge">NeurIPS 2024</abbr></div>

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          <div class="title">Efficiency for Free: Ideal Data Are Transportable Representations</div>
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<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>
                  
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            <em>In Advances in Neural Information Processing Systems (NeurIPS),</em> 2024
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            <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>
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        <div class="col-sm-2 abbr"><abbr class="badge">ICLR 2023</abbr></div>

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          <div class="title">Test-Time Robust Personalization for Federated Learning</div>
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<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>
                  
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            <em>In International Conference on Learning Representations (ICLR),</em> 2023
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            <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>
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        <div class="col-sm-2 abbr"><abbr class="badge">ICLR 2020</abbr></div>

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          <!-- Title -->
          <div class="title">Don’t Use Large Mini-batches, Use Local SGD</div>
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<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&amp;hl=en" target="_blank" rel="noopener noreferrer">Jaggi, Martin</a>
                  
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            <em>In International Conference on Learning Representations (ICLR),</em> 2020
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            <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>
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