Timezone: »
Spotlight
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification
Nan Lu · Shida Lei · Gang Niu · Issei Sato · Masashi Sugiyama
To cope with high annotation costs, training a classifier only from weakly supervised data has attracted a great deal of attention these days. Among various approaches, strengthening supervision from completely unsupervised classification is a promising direction, which typically employs class priors as the only supervision and trains a binary classifier from unlabeled (U) datasets. While existing risk-consistent methods are theoretically grounded with high flexibility, they can learn only from two U sets. In this paper, we propose a new approach for binary classification from $m$ U-sets for $m\ge2$. Our key idea is to consider an auxiliary classification task called surrogate set classification (SSC), which is aimed at predicting from which U set each observed sample is drawn. SSC can be solved by a standard (multi-class) classification method, and we use the SSC solution to obtain the final binary classifier through a certain linear-fractional transformation. We built our method in a flexible and efficient end-to-end deep learning framework and prove it to be classifier-consistent. Through experiments, we demonstrate the superiority of our proposed method over state-of-the-art methods.
Author Information
Nan Lu (The University of Tokyo/RIKEN)
Nan Lu is a Ph.D. student at the Department of Complexity Science and Engineering, the University of Tokyo. Her research interests lie in the fields of weakly supervised learning, learning with real-world constraints, and deep learning.
Shida Lei (The University of Tokyo)
Gang Niu (RIKEN)

Gang Niu is currently an indefinite-term senior research scientist at RIKEN Center for Advanced Intelligence Project.
Issei Sato (University of Tokyo / RIKEN)
Masashi Sugiyama (RIKEN / The University of Tokyo)
Related Events (a corresponding poster, oral, or spotlight)
-
2021 Poster: Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification »
Thu. Jul 22nd 04:00 -- 06:00 PM Room
More from the Same Authors
-
2023 : Invited Talk 3: Masashi Sugiyama (RIKEN & UTokyo) - Data distribution shift »
Masashi Sugiyama -
2023 : Enriching Disentanglement: Definitions to Metrics »
Yivan Zhang · Masashi Sugiyama -
2023 Poster: Mitigating Memorization of Noisy Labels by Clipping the Model Prediction »
Hongxin Wei · HUIPING ZHUANG · RENCHUNZI XIE · Lei Feng · Gang Niu · Bo An · Sharon Li -
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: A Universal Unbiased Method for Classification from Aggregate Observations »
Zixi Wei · Lei Feng · Bo Han · Tongliang Liu · Gang Niu · Xiaofeng Zhu · Heng Tao Shen -
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: Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network »
Shuo Yang · Erkun Yang · Bo Han · Yang Liu · Min Xu · Gang Niu · Tongliang Liu -
2022 Spotlight: Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network »
Shuo Yang · Erkun Yang · Bo Han · Yang Liu · Min Xu · Gang Niu · Tongliang Liu -
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: Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack »
Ruize Gao · Jiongxiao Wang · Kaiwen Zhou · Feng Liu · Binghui Xie · Gang Niu · Bo Han · James Cheng -
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 Spotlight: Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack »
Ruize Gao · Jiongxiao Wang · Kaiwen Zhou · Feng Liu · Binghui Xie · Gang Niu · Bo Han · James Cheng -
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: Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels »
Songhua Wu · Xiaobo Xia · Tongliang Liu · Bo Han · Mingming Gong · Nannan Wang · Haifeng Liu · Gang Niu -
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: 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 Oral: Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization »
Yivan Zhang · Gang Niu · Masashi Sugiyama -
2021 Spotlight: Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels »
Songhua Wu · Xiaobo Xia · Tongliang Liu · Bo Han · Mingming Gong · Nannan Wang · Haifeng Liu · Gang Niu -
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: Searching to Exploit Memorization Effect in Learning with Noisy Labels »
QUANMING YAO · Hansi Yang · Bo Han · Gang Niu · James Kwok -
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 : Spotlight »
Tyler Scott · Kiran Thekumparampil · Jonathan Aigrain · Rene Bidart · Priyadarshini Panda · Dian Ang Yap · Yaniv Yacoby · Raphael Gontijo Lopes · Alberto Marchisio · Erik Englesson · Wanqian Yang · Moritz Graule · Yi Sun · Daniel Kang · Mike Dusenberry · Min Du · Hartmut Maennel · Kunal Menda · Vineet Edupuganti · Luke Metz · David Stutz · Vignesh Srinivasan · Timo Sämann · Vineeth N Balasubramanian · Sina Mohseni · Rob Cornish · Judith Butepage · Zhangyang Wang · Bai Li · Bo Han · Honglin Li · Maksym Andriushchenko · Lukas Ruff · Meet P. Vadera · Yaniv Ovadia · Sunil Thulasidasan · Disi Ji · Gang Niu · Saeed Mahloujifar · Aviral Kumar · SANGHYUK CHUN · Dong Yin · Joyce Xu Xu · Hugo Gomes · Raanan Rohekar -
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 Poster: Evaluating the Variance of Likelihood-Ratio Gradient Estimators »
Seiya Tokui · Issei Sato -
2017 Talk: Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data »
Tomoya Sakai · Marthinus C du Plessis · Gang Niu · Masashi Sugiyama -
2017 Talk: Evaluating the Variance of Likelihood-Ratio Gradient Estimators »
Seiya Tokui · Issei Sato