Unsupervised Domain Adaptation Based on Source-Guided Discrepancy. - Seiichi Kuroki; Nontawat Charoenphakdee; Han Bao 002; Junya Honda; Issei Sato; Masashi Sugiyama (AAAI, 2019)

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)

Imitation Learning from Imperfect Demonstration. - Yueh-Hua Wu; Nontawat Charoenphakdee; Han Bao 002; Voot Tangkaratt; Masashi Sugiyama (ICML, 2019)

On the Calibration of Multiclass Classification with Rejection. - Chenri Ni; Nontawat Charoenphakdee; Junya Honda; Masashi Sugiyama (NeurIPS, 2019)

Positive-Unlabeled Classification under Class Prior Shift and Asymmetric Error. - Nontawat Charoenphakdee; Masashi Sugiyama (SDM, 2019)

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

On Possibility and Impossibility of Multiclass Classification with Rejection. - Chenri Ni; Nontawat Charoenphakdee; Junya Honda; Masashi Sugiyama (CoRR, 2019)

Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization. - Taira Tsuchiya; Nontawat Charoenphakdee; Issei Sato; Masashi Sugiyama (CoRR, 2019)

Learning from Indirect Observations. - Yivan Zhang; Nontawat Charoenphakdee; Masashi Sugiyama (CoRR, 2019)

Learning from Aggregate Observations. - Yivan Zhang; Nontawat Charoenphakdee; Zhenguo Wu; Masashi Sugiyama (NeurIPS, 2020)

Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time. - Hideaki Imamura; Nontawat Charoenphakdee; Futoshi Futami; Issei Sato; Junya Honda; Masashi Sugiyama (CoRR, 2020)

Classification from Triplet Comparison Data. - Zhenghang Cui; Nontawat Charoenphakdee; Issei Sato; Masashi Sugiyama (Neural Comput., 2020)

Robust Imitation Learning from Noisy Demonstrations. - Voot Tangkaratt; Nontawat Charoenphakdee; Masashi Sugiyama (AISTATS, 2021)

On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective. - Nontawat Charoenphakdee; Jayakorn Vongkulbhisal; Nuttapong Chairatanakul; Masashi Sugiyama (CVPR, 2021)

Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous Graph. - Nuttapong Chairatanakul; Noppayut Sriwatanasakdi; Nontawat Charoenphakdee; Xin Liu 020; Tsuyoshi Murata (EMNLP (Findings), 2021)

Classification with Rejection Based on Cost-sensitive Classification. - Nontawat Charoenphakdee; Zhenghang Cui; Yivan Zhang; Masashi Sugiyama (ICML, 2021)

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

Semisupervised Ordinal Regression Based on Empirical Risk Minimization. - Taira Tsuchiya; Nontawat Charoenphakdee; Issei Sato; Masashi Sugiyama (Neural Comput., 2021)

Diffusion models for missing value imputation in tabular data. - Shuhan Zheng; Nontawat Charoenphakdee (CoRR, 2022)

Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification. - Takashi Ishida 001; Ikko Yamane; Nontawat Charoenphakdee; Gang Niu 001; Masashi Sugiyama (ICLR, 2023)

Virtual Human Generative Model: Masked Modeling Approach for Learning Human Characteristics. - Kenta Oono; Nontawat Charoenphakdee; Kotatsu Bito; Zhengyan Gao; Yoshiaki Ota; Shoichiro Yamaguchi; Yohei Sugawara; Shin-ichi Maeda; Kunihiko Miyoshi; Yuki Saito; Koki Tsuda; Hiroshi Maruyama; et al. (CoRR, 2023)

Calm Composite Losses: Being Improper Yet Proper Composite. - Han Bao 002; Nontawat Charoenphakdee (AISTATS, 2025)