| Imitation Learning from Imperfect Demonstration YH Wu, N Charoenphakdee, H Bao, V Tangkaratt, M Sugiyama ICML 2019, 2019 | 163 | 2019 |
| Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements S Takamoto, C Shinagawa, D Motoki, K Nakago, W Li, I Kurata, ... Nature Communications 13 (1), 2991, 2022 | 147 | 2022 |
| On Symmetric Losses for Learning from Corrupted Labels N Charoenphakdee, J Lee, M Sugiyama ICML 2019, 2019 | 104 | 2019 |
| Classification with Rejection Based on Cost-sensitive Classification N Charoenphakdee, Z Cui, Y Zhang, M Sugiyama ICML 2021, 2021 | 73 | 2021 |
| On the Calibration of Multiclass Classification with Rejection C Ni, N Charoenphakdee, J Honda, M Sugiyama NeurIPS 2019, 2019 | 69 | 2019 |
| Unsupervised Domain Adaptation Based on Source-guided Discrepancy S Kuroki, N Charoenphakdee, H Bao, J Honda, I Sato, M Sugiyama AAAI 2019, 2019 | 63 | 2019 |
| Diffusion models for missing value imputation in tabular data S Zheng, N Charoenphakdee NeurIPS Table Representation Learning Workshop 2022, 2022 | 33 | 2022 |
| Robust Imitation Learning from Noisy Demonstrations V Tangkaratt, N Charoenphakdee, M Sugiyama AISTATS 2021, 2021 | 27 | 2021 |
| Learning from Aggregate Observations Y Zhang, N Charoenphakdee, Z Wu, M Sugiyama NeurIPS 2020, 2020 | 27 | 2020 |
| On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective N Charoenphakdee, J Vongkulbhisal, N Chairatanakul, M Sugiyama CVPR 2021, 2021 | 22 | 2021 |
| Classification from Triplet Comparison Data Z Cui, N Charoenphakdee, I Sato, M Sugiyama Neural Computation, 2020 | 22 | 2020 |
| Semi-supervised Ordinal Regression Based on Empirical Risk Minimization T Tsuchiya, N Charoenphakdee, I Sato, M Sugiyama Neural Computation, 2021 | 11 | 2021 |
| Positive-Unlabeled Classification under Class Prior Shift and Asymmetric Error N Charoenphakdee, M Sugiyama SDM 2019, 2019 | 10 | 2019 |
| Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification T Ishida, I Yamane, N Charoenphakdee, G Niu, M Sugiyama ICLR 2023, 2022 | 8 | 2022 |
| 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 from Indirect Observations Y Zhang, N Charoenphakdee, M Sugiyama arXiv preprint arXiv:1910.04394, 2019 | 6 | 2019 |
| Learning Only from Relevant Keywords and Unlabeled Documents N Charoenphakdee, J Lee, Y Jin, D Wanvarie, M Sugiyama EMNLP-IJCNLP 2019, 2019 | 6 | 2019 |
| Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time H Imamura, N Charoenphakdee, F Futami, I Sato, J Honda, M Sugiyama arXiv preprint arXiv:2003.04691, 2020 | 5 | 2020 |
| Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous Graph N Chairatanakul, N Sriwatanasakdi, N Charoenphakdee, X Liu, T Murata Findings of EMNLP 2021, 2021 | 4 | 2021 |
| Assessment of Multidimensional Health Care Parameters Among Adults in Japan for Developing a Virtual Human Generative Model: Protocol for a Cross-sectional Study M Hibi, S Katada, A Kawakami, K Bito, M Ohtsuka, K Sugitani, A Muliandi, ... JMIR Research Protocols 12 (1), e47024, 2023 | 2 | 2023 |
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of Tokyo在 k.u-tokyo.ac.jp 的电子邮件经过验证
