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Equivariant Disentangled Transformation for Domain Generalization under Combination Shift


Aug 03, 2022
Yivan Zhang, Jindong Wang, Xing Xie, Masashi Sugiyama


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Adapting to Online Label Shift with Provable Guarantees


Jul 05, 2022
Yong Bai, Yu-Jie Zhang, Peng Zhao, Masashi Sugiyama, Zhi-Hua Zhou


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Learning from Multiple Unlabeled Datasets with Partial Risk Regularization


Jul 04, 2022
Yuting Tang, Nan Lu, Tianyi Zhang, Masashi Sugiyama


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The Survival Bandit Problem


Jun 07, 2022
Charles Riou, Junya Honda, Masashi Sugiyama


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Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation


Jun 06, 2022
De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama

* accepted by CVPR2022 

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Excess risk analysis for epistemic uncertainty with application to variational inference


Jun 02, 2022
Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama


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Universal approximation property of invertible neural networks


Apr 15, 2022
Isao Ishikawa, Takeshi Teshima, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama

* This paper extends our previous work of the following two papers: "Coupling-based invertible neural networks are universal diffeomorphism approximators" [arXiv:2006.11469] (published as a conference paper in NeurIPS 2020) and "Universal approximation property of neural ordinary differential equations" [arXiv:2012.02414] (presented at DiffGeo4DL Workshop in NeurIPS 2020) 

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Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients


Apr 07, 2022
Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama

* ICLR 2022 camera-ready version 

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On the Effectiveness of Adversarial Training against Backdoor Attacks


Feb 22, 2022
Yinghua Gao, Dongxian Wu, Jingfeng Zhang, Guanhao Gan, Shu-Tao Xia, Gang Niu, Masashi Sugiyama


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Adversarial Attacks and Defense for Non-Parametric Two-Sample Tests


Feb 07, 2022
Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan Kankanhalli


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