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79  <h1>Kento Nozawa</h1>
80    <p class="author">
81      <br />
82      15 July 2026
83  </p>
84

85  <div class="abstract">
86  <h2>Short bio</h2>
87  <p>
88    Kento Nozawa is an engineer at Preferred Networks, Inc.
89    Recently, he has post-trained in-house LLMs, <a href="https://plamo.preferredai.jp/">PLaMo</a>.
90    <!-- He has been working on machine learning, especially self-supervised representation learning. -->
91    <!-- His interest is to understand simple and practical algorithms from theoretical perspective. He's contributed open-source software occasionally not to think about his life. -->
92    He completed his Ph.D. under the supervision of <a href="https://www.ml.is.s.u-tokyo.ac.jp/issei-sato-en">Dr. Issei Sato</a> at <a href="https://www.ml.is.s.u-tokyo.ac.jp/home-en">Issei Sato Lab</a> in <a href="https://www.u-tokyo.ac.jp/en/">The University of Tokyo</a>.
93    Back then, he was working on self-supervised representation learning, especially contrastive representation learning.
94    <!-- During the summer 2019, he visited <a href="https://www.ucl.ac.uk/ai-centre/">UCL AI Centre</a> and Inria Lille Nord Europe <a href="https://team.inria.fr/modal/">Modal team</a> to work on PAC-Bayes and contrastive representation learning. -->
95  </p>
96</div>
97
98<h2 id="selected-journal-and-conference-papers">Selected journal and conference papers</h2>
99
100<ol>
101  <li><a href="https://hermite.jp/">Han Bao</a>, <a href="http://ganow.me/">Yoshihiro Nagano</a> and <strong>Kento Nozawa</strong>. On the Surrogate Gap between Contrastive and Supervised Losses. In <em>ICML</em>, pages 1585–1606, 2022. <a href="https://proceedings.mlr.press/v162/bao22e.html"><code class="language-plaintext highlighter-rouge">paper</code></a>, <a href="https://slideslive.com/38983196/on-the-surrogate-gap-between-contrastive-and-supervised-losses"><code class="language-plaintext highlighter-rouge">video</code></a>, <a href="https://github.com/nzw0301/gap-contrastive-and-supervised-losses"><code class="language-plaintext highlighter-rouge">code</code></a>, <a href="https://hermite.jp/posters/202207_ICML.pdf"><code class="language-plaintext highlighter-rouge">poster</code></a>, <a href="https://arxiv.org/abs/2110.02501"><code class="language-plaintext highlighter-rouge">arXiv</code></a>.<label for="sn-1" class="sidenote-toggle sidenote-number"></label><input type="checkbox" id="sn-1" class="sidenote-toggle" /><span class="sidenote">Alphabetical ordering and equal contribution.</span></li>
102  <li><strong>Kento Nozawa</strong> and <a href="https://www.ml.is.s.u-tokyo.ac.jp/issei-sato-en">Issei Sato</a>. Evaluation Methods for Representation Learning: A Survey. In <em>IJCAI-ECAI Survey Track</em>, pages 5556–5563, 2022. <a href="https://www.ijcai.org/proceedings/2022/0776.pdf"><code class="language-plaintext highlighter-rouge">paper</code></a>, <a href="https://www.ijcai.org/proceedings/2022/video/776"><code class="language-plaintext highlighter-rouge">video</code></a>, <a href="https://speakerdeck.com/nzw0301/evaluation-methods-for-representation-learning-a-survey"><code class="language-plaintext highlighter-rouge">slides</code></a>.<label for="sn-2" class="sidenote-toggle sidenote-number"></label><input type="checkbox" id="sn-2" class="sidenote-toggle" /><span class="sidenote">Extended version: ``Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey’’. 2022. <a href="https://arxiv.org/abs/2204.08226"><code class="language-plaintext highlighter-rouge">arXiv</code></a></span></li>
103  <li><strong>Kento Nozawa</strong> and <a href="https://www.ml.is.s.u-tokyo.ac.jp/issei-sato-en">Issei Sato</a>. Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning. In <em>NeurIPS</em>, pages 5784–5797, 2021. <a href="https://openreview.net/forum?id=pZ5X_svdPQ"><code class="language-plaintext highlighter-rouge">paper</code></a>, <a href="https://speakerdeck.com/nzw0301/understanding-negative-samples-in-instance-discriminative-self-supervised-representation-learning"><code class="language-plaintext highlighter-rouge">slides</code></a>, <a href="https://github.com/nzw0301/Understanding-Negative-Samples"><code class="language-plaintext highlighter-rouge">code</code></a>, <a href="https://drive.google.com/file/d/1uGDY2YrneNF2bFgjh1yMlDeUpVk1GQRL/view?usp=sharing"><code class="language-plaintext highlighter-rouge">poster</code></a>, <a href="https://arxiv.org/abs/2102.06866"><code class="language-plaintext highlighter-rouge">arXiv</code></a>.</li>
104  <li><strong>Kento Nozawa</strong>, <a href="http://www.pascalgermain.info/">Pascal Germain</a> and <a href="https://bguedj.github.io/">Benjamin Guedj</a>. PAC-Bayesian Contrastive Unsupervised Representation Learning. In <em>UAI</em>, pages 21–30, 2020. <a href="https://proceedings.mlr.press/v124/nozawa20a.html"><code class="language-plaintext highlighter-rouge">paper</code></a>, <a href="https://youtu.be/s-PrWBoakw0"><code class="language-plaintext highlighter-rouge">video</code></a>, <a href="assets/pdf/uai2020.pdf"><code class="language-plaintext highlighter-rouge">slides</code></a>, <a href="http://github.com/nzw0301/pb-contrastive"><code class="language-plaintext highlighter-rouge">code</code></a>, <a href="https://arxiv.org/abs/1910.04464"><code class="language-plaintext highlighter-rouge">arXiv</code></a>.</li>
105  <li><a href="https://sites.google.com/site/atsukan82/">Atsunori Kanemura</a>, Yuhsen Cheng, Takumi Kaneko, <strong>Kento Nozawa</strong> and <a href="https://sites.google.com/site/shuichifukunaga/home_e">

105Shuichi Fukunaga</a>. Imputing Missing Values in EEG with Multivariate Autoregressive Models. In <em>EMBC</em>, pages 2639–2642, 2018.</li>
106</ol>
107
108<h2 id="pre-print">Pre-print</h2>
109
110<p>:(</p>
111
112<p>The full publication list is available on <a href="https://scholar.google.co.uk/citations?user=DSdjj8AAAAAJ">Google Scholar</a>.</p>
113
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