| On Symmetric Losses for Learning from Corrupted Labels N Charoenphakdee, J Lee, M Sugiyama Proceedings of 36th International Conference on Machine Learning (ICML2019), 2019 | 105 | 2019 |
| Domain Discrepancy Measure for Complex Models in Unsupervised Domain Adaptation J Lee, N Charoenphakdee, S Kuroki, M Sugiyama arXiv preprint arXiv:1901.10654, 2019 | 7 | 2019 |
| Learning Only from Relevant Keywords and Unlabeled Documents N Charoenphakdee, J Lee, Y Jin, D Wanvarie, M Sugiyama Proceedings of 2019 Conference on Empirical Methods in Natural Language …, 2019 | 6 | 2019 |
| Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits J Lee, J Honda, CK Chiang, M Sugiyama Proceedings of 40th International Conference on Machine Learning (ICML2023), 2023 | 5 | 2023 |
| Thompson Exploration with Best Challenger Rule in Best Arm Identification J Lee, J Honda, M Sugiyama Proceedings of The 14th Asian Conference on Machine Learning (ACML 2023), 2023 | 2 | 2023 |
| A Symmetric Loss Perspective of Reliable Machine Learning N Charoenphakdee, J Lee, M Sugiyama arXiv preprint arXiv:2101.01366, 2021 | 2 | 2021 |
| Follow-the-Perturbed-Leader with Fr\'{e} chet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds J Lee, J Honda, S Ito, M Oh arXiv preprint arXiv:2403.05134, 2024 | | 2024 |
| The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models J Lee, CK Chiang, M Sugiyama Proceedings of 38th AAAI Conference on Artificial Intelligence (AAAI2024), 2023 | | 2023 |
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
