Learning from Complementary Labels. - Takashi Ishida 001; Gang Niu 001; Weihua Hu; Masashi Sugiyama (NIPS, 2017)

Learning from Complementary Labels. - Takashi Ishida 001; Gang Niu 001; Masashi Sugiyama (CoRR, 2017)

Binary Classification from Positive-Confidence Data. - Takashi Ishida 001; Gang Niu 001; Masashi Sugiyama (NeurIPS, 2018)

Complementary-Label Learning for Arbitrary Losses and Models. - Takashi Ishida 001; Gang Niu 001; Aditya Krishna Menon; Masashi Sugiyama (ICML, 2019)

Do We Need Zero Training Loss After Achieving Zero Training Error? - Takashi Ishida 001; Ikko Yamane; Tomoya Sakai 001; Gang Niu 001; Masashi Sugiyama (ICML, 2020)

Learning from Noisy Complementary Labels with Robust Loss Functions. - Hiroki Ishiguro; Takashi Ishida 001; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2022)

LocalDrop: A Hybrid Regularization for Deep Neural Networks. - Ziqing Lu; Chang Xu 002; Bo Du 001; Takashi Ishida 001; Lefei Zhang; Masashi Sugiyama (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Mediated Uncoupled Learning and Validation with Bregman Divergences: Loss Family with Maximal Generality. - Ikko Yamane; Yann Chevaleyre; Takashi Ishida 001; Florian Yger (AISTATS, 2023)

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)

Flooding Regularization for Stable Training of Generative Adversarial Networks. - Iu Yahiro; Takashi Ishida 001; Naoto Yokoya (CoRR, 2023)

Learning with Complementary Labels Revisited: A Consistent Approach via Negative-Unlabeled Learning. - Wei Wang 373; Takashi Ishida 001; Yu-Jie Zhang; Gang Niu 001; Masashi Sugiyama (CoRR, 2023)

Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical. - Wei Wang 373; Takashi Ishida 001; Yu-Jie Zhang; Gang Niu 001; Masashi Sugiyama (ICML, 2024)

How Can I Publish My LLM Benchmark Without Giving the True Answers Away? - Takashi Ishida 001; Thanawat Lodkaew; Ikko Yamane (CoRR, 2025)

Practical estimation of the optimal classification error with soft labels and calibration. - Ryota Ushio; Takashi Ishida 001; Masashi Sugiyama (CoRR, 2025)

Off-Policy Corrected Reward Modeling for Reinforcement Learning from Human Feedback. - Johannes Ackermann; Takashi Ishida 001; Masashi Sugiyama (CoRR, 2025)