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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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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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Rethinking Importance Weighting for Transfer Learning


Dec 19, 2021
Nan Lu, Tianyi Zhang, Tongtong Fang, Takeshi Teshima, Masashi Sugiyama


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Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification


Feb 01, 2021
Shida Lei, Nan Lu, Gang Niu, Issei Sato, Masashi Sugiyama


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Pointwise Binary Classification with Pairwise Confidence Comparisons


Oct 05, 2020
Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama


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A One-step Approach to Covariate Shift Adaptation


Jul 08, 2020
Tianyi Zhang, Ikko Yamane, Nan Lu, Masashi Sugiyama


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Rethinking Importance Weighting for Deep Learning under Distribution Shift


Jun 08, 2020
Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama


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Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach


Oct 20, 2019
Nan Lu, Tianyi Zhang, Gang Niu, Masashi Sugiyama


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On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data


Oct 05, 2018
Nan Lu, Gang Niu, Aditya K. Menon, Masashi Sugiyama


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