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Invited Talk 3: Masashi Sugiyama (RIKEN & UTokyo) - Data distribution shift
Masashi Sugiyama
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Masashi Sugiyama (RIKEN / The University of Tokyo)
More from the Same Authors
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2023 : Enriching Disentanglement: Definitions to Metrics »
Yivan Zhang · Masashi Sugiyama -
2023 Poster: GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks »
Salah GHAMIZI · Jingfeng ZHANG · Maxime Cordy · Mike Papadakis · Masashi Sugiyama · YVES LE TRAON -
2023 Poster: Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation »
Ruijiang Dong · Feng Liu · Haoang Chi · Tongliang Liu · Mingming Gong · Gang Niu · Masashi Sugiyama · Bo Han -
2023 Poster: Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits »
Jongyeong Lee · Junya Honda · Chao-Kai Chiang · Masashi Sugiyama -
2023 Poster: A Category-theoretical Meta-analysis of Definitions of Disentanglement »
Yivan Zhang · Masashi Sugiyama -
2022 Poster: Adversarial Attack and Defense for Non-Parametric Two-Sample Tests »
Xilie Xu · Jingfeng Zhang · Feng Liu · Masashi Sugiyama · Mohan Kankanhalli -
2022 Poster: Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum »
Zeke Xie · Xinrui Wang · Huishuai Zhang · Issei Sato · Masashi Sugiyama -
2022 Spotlight: Adversarial Attack and Defense for Non-Parametric Two-Sample Tests »
Xilie Xu · Jingfeng Zhang · Feng Liu · Masashi Sugiyama · Mohan Kankanhalli -
2022 Oral: Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum »
Zeke Xie · Xinrui Wang · Huishuai Zhang · Issei Sato · Masashi Sugiyama -
2022 Poster: To Smooth or Not? When Label Smoothing Meets Noisy Labels »
Jiaheng Wei · Hangyu Liu · Tongliang Liu · Gang Niu · Masashi Sugiyama · Yang Liu -
2022 Oral: To Smooth or Not? When Label Smoothing Meets Noisy Labels »
Jiaheng Wei · Hangyu Liu · Tongliang Liu · Gang Niu · Masashi Sugiyama · Yang Liu -
2021 Poster: Provably End-to-end Label-noise Learning without Anchor Points »
Xuefeng Li · Tongliang Liu · Bo Han · Gang Niu · Masashi Sugiyama -
2021 Poster: Learning Diverse-Structured Networks for Adversarial Robustness »
Xuefeng Du · Jingfeng Zhang · Bo Han · Tongliang Liu · Yu Rong · Gang Niu · Junzhou Huang · Masashi Sugiyama -
2021 Poster: CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection »
Hanshu YAN · Jingfeng Zhang · Gang Niu · Jiashi Feng · Vincent Tan · Masashi Sugiyama -
2021 Poster: Maximum Mean Discrepancy Test is Aware of Adversarial Attacks »
Ruize Gao · Feng Liu · Jingfeng Zhang · Bo Han · Tongliang Liu · Gang Niu · Masashi Sugiyama -
2021 Spotlight: CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection »
Hanshu YAN · Jingfeng Zhang · Gang Niu · Jiashi Feng · Vincent Tan · Masashi Sugiyama -
2021 Spotlight: Provably End-to-end Label-noise Learning without Anchor Points »
Xuefeng Li · Tongliang Liu · Bo Han · Gang Niu · Masashi Sugiyama -
2021 Spotlight: Learning Diverse-Structured Networks for Adversarial Robustness »
Xuefeng Du · Jingfeng Zhang · Bo Han · Tongliang Liu · Yu Rong · Gang Niu · Junzhou Huang · Masashi Sugiyama -
2021 Spotlight: Maximum Mean Discrepancy Test is Aware of Adversarial Attacks »
Ruize Gao · Feng Liu · Jingfeng Zhang · Bo Han · Tongliang Liu · Gang Niu · Masashi Sugiyama -
2021 Poster: Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences »
Ikko Yamane · Junya Honda · Florian YGER · Masashi Sugiyama -
2021 Poster: Pointwise Binary Classification with Pairwise Confidence Comparisons »
Lei Feng · Senlin Shu · Nan Lu · Bo Han · Miao Xu · Gang Niu · Bo An · Masashi Sugiyama -
2021 Poster: Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification »
Nan Lu · Shida Lei · Gang Niu · Issei Sato · Masashi Sugiyama -
2021 Poster: Learning from Similarity-Confidence Data »
Yuzhou Cao · Lei Feng · Yitian Xu · Bo An · Gang Niu · Masashi Sugiyama -
2021 Poster: Confidence Scores Make Instance-dependent Label-noise Learning Possible »
Antonin Berthon · Bo Han · Gang Niu · Tongliang Liu · Masashi Sugiyama -
2021 Poster: Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization »
Yivan Zhang · Gang Niu · Masashi Sugiyama -
2021 Spotlight: Learning from Similarity-Confidence Data »
Yuzhou Cao · Lei Feng · Yitian Xu · Bo An · Gang Niu · Masashi Sugiyama -
2021 Spotlight: Pointwise Binary Classification with Pairwise Confidence Comparisons »
Lei Feng · Senlin Shu · Nan Lu · Bo Han · Miao Xu · Gang Niu · Bo An · Masashi Sugiyama -
2021 Spotlight: Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences »
Ikko Yamane · Junya Honda · Florian YGER · Masashi Sugiyama -
2021 Spotlight: Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification »
Nan Lu · Shida Lei · Gang Niu · Issei Sato · Masashi Sugiyama -
2021 Oral: Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization »
Yivan Zhang · Gang Niu · Masashi Sugiyama -
2021 Oral: Confidence Scores Make Instance-dependent Label-noise Learning Possible »
Antonin Berthon · Bo Han · Gang Niu · Tongliang Liu · Masashi Sugiyama -
2021 Poster: Lower-Bounded Proper Losses for Weakly Supervised Classification »
Shuhei M Yoshida · Takashi Takenouchi · Masashi Sugiyama -
2021 Poster: Classification with Rejection Based on Cost-sensitive Classification »
Nontawat Charoenphakdee · Zhenghang Cui · Yivan Zhang · Masashi Sugiyama -
2021 Spotlight: Classification with Rejection Based on Cost-sensitive Classification »
Nontawat Charoenphakdee · Zhenghang Cui · Yivan Zhang · Masashi Sugiyama -
2021 Spotlight: Lower-Bounded Proper Losses for Weakly Supervised Classification »
Shuhei M Yoshida · Takashi Takenouchi · Masashi Sugiyama -
2021 Poster: Large-Margin Contrastive Learning with Distance Polarization Regularizer »
Shuo Chen · Gang Niu · Chen Gong · Jun Li · Jian Yang · Masashi Sugiyama -
2021 Poster: Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization »
Zeke Xie · Li Yuan · Zhanxing Zhu · Masashi Sugiyama -
2021 Spotlight: Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization »
Zeke Xie · Li Yuan · Zhanxing Zhu · Masashi Sugiyama -
2021 Spotlight: Large-Margin Contrastive Learning with Distance Polarization Regularizer »
Shuo Chen · Gang Niu · Chen Gong · Jun Li · Jian Yang · Masashi Sugiyama -
2020 Poster: Few-shot Domain Adaptation by Causal Mechanism Transfer »
Takeshi Teshima · Issei Sato · Masashi Sugiyama -
2020 Poster: Do We Need Zero Training Loss After Achieving Zero Training Error? »
Takashi Ishida · Ikko Yamane · Tomoya Sakai · Gang Niu · Masashi Sugiyama -
2020 Poster: Progressive Identification of True Labels for Partial-Label Learning »
Jiaqi Lv · Miao Xu · LEI FENG · Gang Niu · Xin Geng · Masashi Sugiyama -
2020 Poster: Online Dense Subgraph Discovery via Blurred-Graph Feedback »
Yuko Kuroki · Atsushi Miyauchi · Junya Honda · Masashi Sugiyama -
2020 Poster: SIGUA: Forgetting May Make Learning with Noisy Labels More Robust »
Bo Han · Gang Niu · Xingrui Yu · QUANMING YAO · Miao Xu · Ivor Tsang · Masashi Sugiyama -
2020 Poster: Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels »
Yu-Ting Chou · Gang Niu · Hsuan-Tien (Tien) Lin · Masashi Sugiyama -
2020 Poster: Attacks Which Do Not Kill Training Make Adversarial Learning Stronger »
Jingfeng Zhang · Xilie Xu · Bo Han · Gang Niu · Lizhen Cui · Masashi Sugiyama · Mohan Kankanhalli -
2020 Poster: Accelerating the diffusion-based ensemble sampling by non-reversible dynamics »
Futoshi Futami · Issei Sato · Masashi Sugiyama -
2020 Poster: Variational Imitation Learning with Diverse-quality Demonstrations »
Voot Tangkaratt · Bo Han · Mohammad Emtiyaz Khan · Masashi Sugiyama -
2020 Poster: Learning with Multiple Complementary Labels »
LEI FENG · Takuo Kaneko · Bo Han · Gang Niu · Bo An · Masashi Sugiyama -
2020 Poster: Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis »
Yusuke Tsuzuku · Issei Sato · Masashi Sugiyama -
2019 Poster: Classification from Positive, Unlabeled and Biased Negative Data »
Yu-Guan Hsieh · Gang Niu · Masashi Sugiyama -
2019 Poster: Complementary-Label Learning for Arbitrary Losses and Models »
Takashi Ishida · Gang Niu · Aditya Menon · Masashi Sugiyama -
2019 Oral: Complementary-Label Learning for Arbitrary Losses and Models »
Takashi Ishida · Gang Niu · Aditya Menon · Masashi Sugiyama -
2019 Oral: Classification from Positive, Unlabeled and Biased Negative Data »
Yu-Guan Hsieh · Gang Niu · Masashi Sugiyama -
2019 Poster: How does Disagreement Help Generalization against Label Corruption? »
Xingrui Yu · Bo Han · Jiangchao Yao · Gang Niu · Ivor Tsang · Masashi Sugiyama -
2019 Oral: How does Disagreement Help Generalization against Label Corruption? »
Xingrui Yu · Bo Han · Jiangchao Yao · Gang Niu · Ivor Tsang · Masashi Sugiyama -
2019 Poster: Imitation Learning from Imperfect Demonstration »
Yueh-Hua Wu · Nontawat Charoenphakdee · Han Bao · Voot Tangkaratt · Masashi Sugiyama -
2019 Poster: On Symmetric Losses for Learning from Corrupted Labels »
Nontawat Charoenphakdee · Jongyeong Lee · Masashi Sugiyama -
2019 Oral: Imitation Learning from Imperfect Demonstration »
Yueh-Hua Wu · Nontawat Charoenphakdee · Han Bao · Voot Tangkaratt · Masashi Sugiyama -
2019 Oral: On Symmetric Losses for Learning from Corrupted Labels »
Nontawat Charoenphakdee · Jongyeong Lee · Masashi Sugiyama -
2018 Poster: Classification from Pairwise Similarity and Unlabeled Data »
Han Bao · Gang Niu · Masashi Sugiyama -
2018 Oral: Classification from Pairwise Similarity and Unlabeled Data »
Han Bao · Gang Niu · Masashi Sugiyama -
2018 Poster: Does Distributionally Robust Supervised Learning Give Robust Classifiers? »
Weihua Hu · Gang Niu · Issei Sato · Masashi Sugiyama -
2018 Oral: Does Distributionally Robust Supervised Learning Give Robust Classifiers? »
Weihua Hu · Gang Niu · Issei Sato · Masashi Sugiyama -
2018 Poster: Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model »
Hideaki Imamura · Issei Sato · Masashi Sugiyama -
2018 Oral: Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model »
Hideaki Imamura · Issei Sato · Masashi Sugiyama -
2017 Poster: Learning Discrete Representations via Information Maximizing Self-Augmented Training »
Weihua Hu · Takeru Miyato · Seiya Tokui · Eiichi Matsumoto · Masashi Sugiyama -
2017 Talk: Learning Discrete Representations via Information Maximizing Self-Augmented Training »
Weihua Hu · Takeru Miyato · Seiya Tokui · Eiichi Matsumoto · Masashi Sugiyama -
2017 Poster: Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data »
Tomoya Sakai · Marthinus C du Plessis · Gang Niu · Masashi Sugiyama -
2017 Talk: Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data »
Tomoya Sakai · Marthinus C du Plessis · Gang Niu · Masashi Sugiyama