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2020 – today
- 2023
[j176]Yosuke Otsubo, Naoya Otani, Megumi Chikasue, Mineyuki Nishino, Masashi Sugiyama:
Root cause estimation of faults in production processes: a novel approach inspired by approximate Bayesian computation. Int. J. Prod. Res. 61(5): 1556-1574 (2023)- [j175]Shota Nakajima, Masashi Sugiyama:
Positive-unlabeled classification under class-prior shift: a prior-invariant approach based on density ratio estimation. Mach. Learn. 112(3): 889-919 (2023) - [j174]Zhenguo Wu, Jiaqi Lv, Masashi Sugiyama:
Learning With Proper Partial Labels. Neural Comput. 35(1): 58-81 (2023) - [j173]Tingting Zhao, Ying Wang, Wei Sun, Yarui Chen, Gang Niu, Masashi Sugiyama:
Representation learning for continuous action spaces is beneficial for efficient policy learning. Neural Networks 159: 137-152 (2023) - [j172]Chen Gong
, Yongliang Ding, Bo Han, Gang Niu, Jian Yang, Jane You, Dacheng Tao, Masashi Sugiyama:
Class-Wise Denoising for Robust Learning Under Label Noise. IEEE Trans. Pattern Anal. Mach. Intell. 45(3): 2835-2848 (2023) - [c261]Takashi Ishida, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu, Masashi Sugiyama:
Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification. ICLR 2023 - [c260]Xin-Qiang Cai, Yao-Xiang Ding, Zi-Xuan Chen, Yuan Jiang, Masashi Sugiyama, Zhi-Hua Zhou:
Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning. ICLR 2023 - [c259]Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han:
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. ICML 2023: 8260-8275 - [c258]Salah Ghamizi, Jingfeng Zhang, Maxime Cordy, Mike Papadakis, Masashi Sugiyama, Yves Le Traon:
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks. ICML 2023: 11255-11282 - [c257]Jongyeong Lee, Junya Honda, Chao-Kai Chiang, Masashi Sugiyama:
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits. ICML 2023: 18810-18851 - [c256]Yivan Zhang, Masashi Sugiyama:
A Category-theoretical Meta-analysis of Definitions of Disentanglement. ICML 2023: 41596-41612 - [i209]Jongyeong Lee, Junya Honda, Chao-Kai Chiang, Masashi Sugiyama:
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits. CoRR abs/2302.01544 (2023) - [i208]Yu-Jie Zhang, Zhen-Yu Zhang, Peng Zhao, Masashi Sugiyama:
Adapting to Continuous Covariate Shift via Online Density Ratio Estimation. CoRR abs/2302.02552 (2023) - [i207]Salah Ghamizi, Jingfeng Zhang, Maxime Cordy, Mike Papadakis, Masashi Sugiyama, Yves Le Traon:
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks. CoRR abs/2302.02907 (2023) - [i206]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection. CoRR abs/2302.03857 (2023) - [i205]Jongyeong Lee, Chao-Kai Chiang, Masashi Sugiyama:
Asymptotically Optimal Thompson Sampling Based Policy for the Uniform Bandits and the Gaussian Bandits. CoRR abs/2302.14407 (2023) - [i204]Jiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu, Sijia Liu, Masashi Sugiyama, Yang Liu:
Fairness Improves Learning from Noisily Labeled Long-Tailed Data. CoRR abs/2303.12291 (2023) - [i203]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization. CoRR abs/2305.00374 (2023) - [i202]Jingfeng Zhang, Bo Song, Bo Han, Lei Liu, Gang Niu, Masashi Sugiyama:
Assessing Vulnerabilities of Adversarial Learning Algorithm through Poisoning Attacks. CoRR abs/2305.00399 (2023) - [i201]Ming-Kun Xie, Jiahao Xiao, Hao-Zhe Liu, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning. CoRR abs/2305.02795 (2023) - [i200]Yivan Zhang, Masashi Sugiyama:
A Category-theoretical Meta-analysis of Definitions of Disentanglement. CoRR abs/2305.06886 (2023) - [i199]Wei-I Lin, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama:
Enhancing Label Sharing Efficiency in Complementary-Label Learning with Label Augmentation. CoRR abs/2305.08344 (2023) - [i198]Sora Satake, Yoshihiro Nagano, Masashi Sugiyama, Masahiro Fujiwara, Yasutoshi Makino, Hiroyuki Shinoda:
Analysis of Pleasantness Evoked by Various Airborne Ultrasound Tactile Stimuli Using Pairwise Comparisons and the Bradley-Terry Model. CoRR abs/2305.09412 (2023) - [i197]Yivan Zhang, Masashi Sugiyama:
Enriching Disentanglement: Definitions to Metrics. CoRR abs/2305.11512 (2023) - [i196]Hao Chen, Ankit Shah, Jindong Wang, Ran Tao, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj:
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations. CoRR abs/2305.12715 (2023) - [i195]Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama:
Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems. CoRR abs/2305.14690 (2023) - [i194]Jingfeng Zhang, Bo Song, Haohan Wang, Bo Han, Tongliang Liu, Lei Liu, Masashi Sugiyama:
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning. CoRR abs/2305.18377 (2023) - [i193]Yuhao Wu, Xiaobo Xia, Jun Yu, Bo Han, Gang Niu, Masashi Sugiyama, Tongliang Liu:
Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision. CoRR abs/2306.07036 (2023) - [i192]Shintaro Nakamura, Masashi Sugiyama:
Combinatorial Pure Exploration of Multi-Armed Bandit with a Real Number Action Class. CoRR abs/2306.09202 (2023) - [i191]Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han:
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. CoRR abs/2307.05948 (2023) - [i190]Jialiang Tang, Shuo Chen, Gang Niu, Masashi Sugiyama, Chen Gong:
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images. CoRR abs/2307.11469 (2023) - [i189]Penghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Multi-Label Knowledge Distillation. CoRR abs/2308.06453 (2023) - [i188]Shintaro Nakamura, Masashi Sugiyama:
Thompson Sampling for Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit. CoRR abs/2308.10238 (2023) - [i187]Chao-Kai Chiang, Masashi Sugiyama:
Unified Risk Analysis for Weakly Supervised Learning. CoRR abs/2309.08216 (2023) - 2022
- [j171]Akira Tanimoto, So Yamada, Takashi Takenouchi, Masashi Sugiyama, Hisashi Kashima:
Improving imbalanced classification using near-miss instances. Expert Syst. Appl. 201: 117130 (2022) - [j170]Hiroki Ishiguro, Takashi Ishida, Masashi Sugiyama:
Learning from Noisy Complementary Labels with Robust Loss Functions. IEICE Trans. Inf. Syst. 105-D(2): 364-376 (2022) - [j169]Yuangang Pan, Ivor W. Tsang, Weijie Chen, Gang Niu, Masashi Sugiyama:
Fast and Robust Rank Aggregation against Model Misspecification. J. Mach. Learn. Res. 23: 23:1-23:35 (2022) - [j168]Songhua Wu, Tongliang Liu, Bo Han, Jun Yu, Gang Niu, Masashi Sugiyama:
Learning from Noisy Pairwise Similarity and Unlabeled Data. J. Mach. Learn. Res. 23: 307:1-307:34 (2022) - [j167]Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama:
Discovering diverse solutions in deep reinforcement learning by maximizing state-action-based mutual information. Neural Networks 152: 90-104 (2022) - [j166]Yutaka Matsuo, Yann LeCun, Maneesh Sahani, Doina Precup, David Silver, Masashi Sugiyama, Eiji Uchibe, Jun Morimoto:
Deep learning, reinforcement learning, and world models. Neural Networks 152: 267-275 (2022) - [j165]Kenji Doya, Karl J. Friston, Masashi Sugiyama, Joshua B. Tenenbaum:
Neural Networks special issue on Artificial Intelligence and Brain Science. Neural Networks 155: 328-329 (2022) - [j164]Chen Gong, Jian Yang, Jane You, Masashi Sugiyama:
Centroid Estimation With Guaranteed Efficiency: A General Framework for Weakly Supervised Learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(6): 2841-2855 (2022) - [j163]Ziqing Lu, Chang Xu, Bo Du, Takashi Ishida, Lefei Zhang, Masashi Sugiyama:
LocalDrop: A Hybrid Regularization for Deep Neural Networks. IEEE Trans. Pattern Anal. Mach. Intell. 44(7): 3590-3601 (2022) - [j162]Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Lizhen Cui, Gang Niu, Masashi Sugiyama:
NoiLin: Improving adversarial training and correcting stereotype of noisy labels. Trans. Mach. Learn. Res. 2022 (2022) - [c255]Shintaro Nakamura, Han Bao, Masashi Sugiyama:
Robust computation of optimal transport by β-potential regularization. ACML 2022: 770-785 - [c254]Yuting Tang, Nan Lu, Tianyi Zhang, Masashi Sugiyama:
Multi-class Classification from Multiple Unlabeled Datasets with Partial Risk Regularization. ACML 2022: 990-1005 - [c253]Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama:
Pairwise Supervision Can Provably Elicit a Decision Boundary. AISTATS 2022: 2618-2640 - [c252]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Predictive variational Bayesian inference as risk-seeking optimization. AISTATS 2022: 5051-5083 - [c251]Masashi Sugiyama, Tongliang Liu, Bo Han, Yang Liu, Gang Niu:
Learning and Mining with Noisy Labels. CIKM 2022: 5152-5155 - [c250]De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama:
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. CVPR 2022: 16609-16618 - [c249]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. ICLR 2022 - [c248]Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama:
Federated Learning from Only Unlabeled Data with Class-conditional-sharing Clients. ICLR 2022 - [c247]Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama:
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. ICLR 2022 - [c246]Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao:
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning. ICLR 2022 - [c245]Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama:
Exploiting Class Activation Value for Partial-Label Learning. ICLR 2022 - [c244]Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu:
To Smooth or Not? When Label Smoothing Meets Noisy Labels. ICML 2022: 23589-23614 - [c243]Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama:
Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum. ICML 2022: 24430-24459 - [c242]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Adversarial Attack and Defense for Non-Parametric Two-Sample Tests. ICML 2022: 24743-24769 - [c241]Hanshu Yan, Jingfeng Zhang, Jiashi Feng, Masashi Sugiyama, Vincent Y. F. Tan:
Towards Adversarially Robust Deep Image Denoising. IJCAI 2022: 1516-1522 - [c240]Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama:
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. NeurIPS 2022 - [c239]Shuo Chen, Chen Gong, Jun Li, Jian Yang, Gang Niu, Masashi Sugiyama:
Learning Contrastive Embedding in Low-Dimensional Space. NeurIPS 2022 - [c238]Yong Bai, Yu-Jie Zhang, Peng Zhao, Masashi Sugiyama, Zhi-Hua Zhou:
Adapting to Online Label Shift with Provable Guarantees. NeurIPS 2022 - [c237]Yuzhou Cao, Tianchi Cai, Lei Feng, Lihong Gu, Jinjie Gu, Bo An, Gang Niu, Masashi Sugiyama:
Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses. NeurIPS 2022 - [c236]Sen Cui, Jingfeng Zhang, Jian Liang, Bo Han, Masashi Sugiyama, Changshui Zhang:
Synergy-of-Experts: Collaborate to Improve Adversarial Robustness. NeurIPS 2022 - [i186]Hanshu Yan, Jingfeng Zhang, Jiashi Feng, Masashi Sugiyama, Vincent Y. F. Tan:
Towards Adversarially Robust Deep Image Denoising. CoRR abs/2201.04397 (2022) - [i185]Takashi Ishida, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu, Masashi Sugiyama:
Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification. CoRR abs/2202.00395 (2022) - [i184]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Adversarial Attacks and Defense for Non-Parametric Two-Sample Tests. CoRR abs/2202.03077 (2022) - [i183]Yinghua Gao, Dongxian Wu, Jingfeng Zhang, Guanhao Gan, Shu-Tao Xia, Gang Niu, Masashi Sugiyama:
On the Effectiveness of Adversarial Training against Backdoor Attacks. CoRR abs/2202.10627 (2022) - [i182]Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama:
Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients. CoRR abs/2204.03304 (2022) - [i181]Isao Ishikawa, Takeshi Teshima, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama:
Universal approximation property of invertible neural networks. CoRR abs/2204.07415 (2022) - [i180]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Excess risk analysis for epistemic uncertainty with application to variational inference. CoRR abs/2206.01606 (2022) - [i179]De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama:
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. CoRR abs/2206.02791 (2022) - [i178]Charles Riou, Junya Honda, Masashi Sugiyama:
The Survival Bandit Problem. CoRR abs/2206.03019 (2022) - [i177]Yuting Tang, Nan Lu, Tianyi Zhang, Masashi Sugiyama:
Learning from Multiple Unlabeled Datasets with Partial Risk Regularization. CoRR abs/2207.01555 (2022) - [i176]Yong Bai, Yu-Jie Zhang, Peng Zhao, Masashi Sugiyama, Zhi-Hua Zhou:
Adapting to Online Label Shift with Provable Guarantees. CoRR abs/2207.02121 (2022) - [i175]Yivan Zhang, Jindong Wang, Xing Xie, Masashi Sugiyama:
Equivariant Disentangled Transformation for Domain Generalization under Combination Shift. CoRR abs/2208.02011 (2022) - [i174]Nobutaka Ito, Masashi Sugiyama:
Audio Signal Enhancement with Learning from Positive and Unlabelled Data. CoRR abs/2210.15143 (2022) - [i173]Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama:
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. CoRR abs/2211.00269 (2022) - [i172]Tingting Zhao, Ying Wang, Wei Sun, Yarui Chen, Gang Niu, Masashi Sugiyama:
Representation Learning for Continuous Action Spaces is Beneficial for Efficient Policy Learning. CoRR abs/2211.13257 (2022) - [i171]Shintaro Nakamura, Han Bao, Masashi Sugiyama:
Robust computation of optimal transport by β-potential regularization. CoRR abs/2212.13251 (2022) - 2021
- [j161]Motoya Ohnishi, Gennaro Notomista, Masashi Sugiyama, Magnus Egerstedt:
Constraint learning for control tasks with limited duration barrier functions. Autom. 127: 109504 (2021) - [j160]Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Information-Theoretic Representation Learning for Positive-Unlabeled Classification. Neural Comput. 33(1): 244-268 (2021) - [j159]Takuya Shimada, Han Bao, Issei Sato, Masashi Sugiyama:
Classification From Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization. Neural Comput. 33(5): 1234-1268 (2021) - [j158]Wenkai Xu, Gang Niu, Aapo Hyvärinen, Masashi Sugiyama:
Direction Matters: On Influence-Preserving Graph Summarization and Max-Cut Principle for Directed Graphs. Neural Comput. 33(8): 2128-2162 (2021) - [j157]Zeke Xie, Fengxiang He, Shaopeng Fu, Issei Sato, Dacheng Tao, Masashi Sugiyama:
Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting. Neural Comput. 33(8): 2163-2192 (2021) - [j156]Taira Tsuchiya, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama:
Semisupervised Ordinal Regression Based on Empirical Risk Minimization. Neural Comput. 33(12): 3361-3412 (2021) - [j155]Tianyi Zhang, Ikko Yamane, Nan Lu, Masashi Sugiyama:
A One-Step Approach to Covariate Shift Adaptation. SN Comput. Sci. 2(4): 319 (2021) - [c235]Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama:
Robust Imitation Learning from Noisy Demonstrations. AISTATS 2021: 298-306 - [c234]Han Bao, Masashi Sugiyama:
Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation. AISTATS 2021: 1648-1656 - [c233]Masahiro Fujisawa, Takeshi Teshima, Issei Sato, Masashi Sugiyama:
γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator. AISTATS 2021: 1783-1791 - [c232]Paavo Parmas, Masashi Sugiyama:
A unified view of likelihood ratio and reparameterization gradients. AISTATS 2021: 4078-4086 - [c231]Masashi Sugiyama:
Mixture Proportion Estimation in Weakly Supervised Learning. CIKM Workshops 2021 - [c230]Nontawat Charoenphakdee, Jayakorn Vongkulbhisal, Nuttapong Chairatanakul, Masashi Sugiyama:
On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective. CVPR 2021: 5202-5211 - [c229]Alon Jacovi, Gang Niu, Yoav Goldberg, Masashi Sugiyama:
Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning. EACL 2021: 581-592 - [c228]Zeke Xie, Issei Sato, Masashi Sugiyama:
A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima. ICLR 2021 - [c227]Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, Mohan S. Kankanhalli:
Geometry-aware Instance-reweighted Adversarial Training. ICLR 2021 - [c226]Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama:
Confidence Scores Make Instance-dependent Label-noise Learning Possible. ICML 2021: 825-836 - [c225]Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama:
Learning from Similarity-Confidence Data. ICML 2021: 1272-1282 - [c224]Nontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang, Masashi Sugiyama:
Classification with Rejection Based on Cost-sensitive Classification. ICML 2021: 1507-1517 - [c223]Shuo Chen, Gang Niu, Chen Gong, Jun Li, Jian Yang, Masashi Sugiyama:
Large-Margin Contrastive Learning with Distance Polarization Regularizer. ICML 2021: 1673-1683 - [c222]Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama:
Learning Diverse-Structured Networks for Adversarial Robustness. ICML 2021: 2880-2891 - [c221]Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama:
Pointwise Binary Classification with Pairwise Confidence Comparisons. ICML 2021: 3252-3262 - [c220]Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama:
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks. ICML 2021: 3564-3575 - [c219]Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama:
Provably End-to-end Label-noise Learning without Anchor Points. ICML 2021: 6403-6413 - [c218]Nan Lu, Shida Lei, Gang Niu, Issei Sato, Masashi Sugiyama:
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. ICML 2021: 7134-7144 - [c217]Zeke Xie, Li Yuan, Zhanxing Zhu, Masashi Sugiyama:
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization. ICML 2021: 11448-11458 - [c216]Ikko Yamane, Junya Honda, Florian Yger, Masashi Sugiyama:
Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences. ICML 2021: 11637-11647 - [c215]Hanshu Yan, Jingfeng Zhang, Gang Niu, Jiashi Feng, Vincent Y. F. Tan, Masashi Sugiyama:
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection. ICML 2021: 11693-11703 - [c214]Shuhei M. Yoshida, Takashi Takenouchi, Masashi Sugiyama:
Lower-Bounded Proper Losses for Weakly Supervised Classification. ICML 2021: 12110-12120 - [c213]Yivan Zhang, Gang Niu, Masashi Sugiyama:
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. ICML 2021: 12501-12512 - [c212]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Loss function based second-order Jensen inequality and its application to particle variational inference. NeurIPS 2021: 6803-6815 - [c211]Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Probabilistic Margins for Instance Reweighting in Adversarial Training. NeurIPS 2021: 23258-23269 - [c210]Soham Dan, Han Bao, Masashi Sugiyama:
Learning from Noisy Similar and Dissimilar Data. ECML/PKDD (2) 2021: 233-249 - [c209]Takeshi Teshima, Masashi Sugiyama:
Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentation. UAI 2021: 86-96 - [i170]Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama:
A Symmetric Loss Perspective of Reliable Machine Learning. CoRR abs/2101.01366 (2021) - [i169]Masato Ishii, Masashi Sugiyama:
Source-free Domain Adaptation via Distributional Alignment by Matching Batch Normalization Statistics. CoRR abs/2101.10842 (2021) - [i168]Shida Lei, Nan Lu, Gang Niu, Issei Sato, Masashi Sugiyama:
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. CoRR abs/2102.00678 (2021) - [i167]Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama:
Learning Diverse-Structured Networks for Adversarial Robustness. CoRR abs/2102.01886 (2021) - [i166]Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama:
Provably End-to-end Label-Noise Learning without Anchor Points. CoRR abs/2102.02400 (2021) - [i165]Yivan Zhang, Gang Niu, Masashi Sugiyama:
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. CoRR abs/2102.02414 (2021) - [i164]Jianing Zhu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Hongxia Yang, Mohan S. Kankanhalli, Masashi Sugiyama:
Understanding the Interaction of Adversarial Training with Noisy Labels. CoRR abs/2102.03482 (2021) - [i163]