Search Results for author: Senlin Shu

Found 4 papers, 1 papers with code

A Generalized Unbiased Risk Estimator for Learning with Augmented Classes

1 code implementation12 Jun 2023 Senlin Shu, Shuo He, Haobo Wang, Hongxin Wei, Tao Xiang, Lei Feng

In this paper, we propose a generalized URE that can be equipped with arbitrary loss functions while maintaining the theoretical guarantees, given unlabeled data for LAC.

Multi-class Classification

Multi-Class Classification from Single-Class Data with Confidences

no code implementations16 Jun 2021 Yuzhou Cao, Lei Feng, Senlin Shu, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama

We show that without any assumptions on the loss functions, models, and optimizers, we can successfully learn a multi-class classifier from only data of a single class with a rigorous consistency guarantee when confidences (i. e., the class-posterior probabilities for all the classes) are available.

Classification Multi-class Classification

Pointwise Binary Classification with Pairwise Confidence Comparisons

no code implementations5 Oct 2020 Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama

To alleviate the data requirement for training effective binary classifiers in binary classification, many weakly supervised learning settings have been proposed.

Binary Classification Classification +2

Incorporating Multiple Cluster Centers for Multi-Label Learning

no code implementations17 Apr 2020 Senlin Shu, Fengmao Lv, Yan Yan, Li Li, Shuo He, Jun He

In this article, we propose to leverage the data augmentation technique to improve the performance of multi-label learning.

Clustering Data Augmentation +1

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