Kento Nozawa is a research scientist at IBM Research – Tokyo. He has been working on machine learning, especially self-supervised representation learning. 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.
Selected Journal / Conference papers
- Han Bao, Yoshihiro Nagano and Kento Nozawa. On the Surrogate Gap between Contrastive and Supervised Losses. In ICML, pages 1585–1606, 2022.
arXiv.Alphabetical ordering and equal contribution.
- Kento Nozawa and Issei Sato. Evaluation Methods for Representation Learning: A Survey. In IJCAI-ECAI Survey Track, pages 5556–5563, 2022.
slides.Extended version: ``Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey’’. 2022.
- Kento Nozawa and Issei Sato. Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning. In NeurIPS, pages 5784–5797, 2021.
- Kento Nozawa, Pascal Germain and Benjamin Guedj. PAC-Bayesian Contrastive Unsupervised Representation Learning. In UAI, pages 21–30, 2020.
- 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.
The full publication list is available on Google Scholar.