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Masashi Sugiyama
222 publications
Bo Han
121 publications
Qi Dou
97 publications
Gang Niu
93 publications
Tianyi Zhang
86 publications
Xiaoxiao Li
78 publications
Bo An
62 publications
Issei Sato
60 publications
Lei Feng
38 publications
Robert C. Williamson
28 publications
Zhao Wang
26 publications

∙ 07/17/2023

A General Framework for Learning under Corruption: Label Noise, Attribute Noise, and Beyond

Corruption is frequently observed in collected data and has been extensi...

0 Laura Iacovissi, et al. ∙

∙ 05/24/2023

Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems

Distribution shift (DS) may have two levels: the distribution itself cha...

0 Tongtong Fang, et al. ∙

∙ 07/04/2022

Learning from Multiple Unlabeled Datasets with Partial Risk Regularization

Recent years have witnessed a great success of supervised deep learning,...

0 Yuting Tang, et al. ∙

∙ 04/07/2022

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

Supervised federated learning (FL) enables multiple clients to share the...

25 Nan Lu, et al. ∙

∙ 12/19/2021

Rethinking Importance Weighting for Transfer Learning

A key assumption in supervised learning is that training and test data f...

0 Nan Lu, et al. ∙

∙ 02/01/2021

Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification

To cope with high annotation costs, training a classifier only from weak...

0 Shida Lei, et al. ∙

∙ 10/05/2020

Pointwise Binary Classification with Pairwise Confidence Comparisons

Ordinary (pointwise) binary classification aims to learn a binary classi...

3 Lei Feng, et al. ∙

∙ 07/08/2020

A One-step Approach to Covariate Shift Adaptation

A default assumption in many machine learning scenarios is that the trai...

0 Tianyi Zhang, et al. ∙

∙ 06/08/2020

Rethinking Importance Weighting for Deep Learning under Distribution Shift

Under distribution shift (DS) where the training data distribution diffe...

0 Tongtong Fang, et al. ∙

∙ 10/20/2019

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

From two unlabeled (U) datasets with different class priors, we can trai...

0 Nan Lu, et al. ∙

∙ 08/31/2018

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

Empirical risk minimization (ERM), with proper loss function and regular...

0 Nan Lu, et al. ∙

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