Learning Only from Relevant Keywords and Unlabeled Documents. - Nontawat Charoenphakdee; Jongyeong Lee; Yiping Jin; Dittaya Wanvarie; Masashi Sugiyama (EMNLP/IJCNLP (1), 2019)

On Symmetric Losses for Learning from Corrupted Labels. - Nontawat Charoenphakdee; Jongyeong Lee; Masashi Sugiyama (ICML, 2019)

Domain Discrepancy Measure Using Complex Models in Unsupervised Domain Adaptation. - Jongyeong Lee; Nontawat Charoenphakdee; Seiichi Kuroki; Masashi Sugiyama (CoRR, 2019)

A Symmetric Loss Perspective of Reliable Machine Learning. - Nontawat Charoenphakdee; Jongyeong Lee; Masashi Sugiyama (CoRR, 2021)

Thompson Exploration with Best Challenger Rule in Best Arm Identification. - Jongyeong Lee; Junya Honda; Masashi Sugiyama (ACML, 2023)

Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits. - Jongyeong Lee; Junya Honda; Chao-Kai Chiang; Masashi Sugiyama (ICML, 2023)

Asymptotically Optimal Thompson Sampling Based Policy for the Uniform Bandits and the Gaussian Bandits. - Jongyeong Lee; Chao-Kai Chiang; Masashi Sugiyama (CoRR, 2023)

The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models. - Jongyeong Lee; Chao-Kai Chiang; Masashi Sugiyama (AAAI, 2024)

Follow-the-Perturbed-Leader with Fréchet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds. - Jongyeong Lee; Junya Honda; Shinji Ito; Min-hwan Oh (COLT, 2024)