Kento Nozawa
Bio
Kento Nozawa is a 5th year Ph.D. student who does research on machine learning. His current research interest is self-supervised representation learning via theoretical tools such as PAC-Bayes theory. He is supervised by Dr. Issei Sato at Issei Sato Lab at The University of Tokyo. He is also a part-time research assistant at RIKEN AIP. During summer 2019, he visited UCL AI Centre and Inria Lille Nord Europe Modal team to work on PAC-Bayes and contrastive representation learning. In order to live, he has been working for Optuna as a part-time machine learning engineer at Preferred Networks, Inc. since April 2021.
Hopefully, he’ll be graduating in Sept. 2022, so he is on the job market.
Journal / Conference papers
- Han Bao, Yoshihiro Nagano and Kento Nozawa. On the Surrogate Gap between Contrastive and Supervised Losses. In ICML, 2022.
arXiv
.Alphabetical ordering and equal contribution. - Kento Nozawa and Issei Sato. Evaluation Methods for Representation Learning: A Survey. In IJCAI-ECAI Survey Track, 2022.Extended version: ``Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey’’. 2022.
arXiv
- Kento Nozawa and Issei Sato. Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning. In NeurIPS, pages 5784–5797, 2021.
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. - Kento Nozawa, Pascal Germain and Benjamin Guedj. PAC-Bayesian Contrastive Unsupervised Representation Learning. In UAI, pages 21–30, 2020.
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. - Atsunori Kanemura, Yuhsen Cheng, Takumi Kaneko, Kento Nozawa and Shuichi Fukunaga. Imputing Missing Values in EEG with Multivariate Autoregressive Models. In EMBC, pages 2639–2642, 2018.
Pre-print
- Kento Nozawa and Issei Sato. PAC-Bayes Analysis of Sentence Representation. 2019.
arXiv
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