Search Results for author: Yivan Zhang

Found 6 papers, 4 papers with code

Equivariant Disentangled Transformation for Domain Generalization under Combination Shift

no code implementations3 Aug 2022 Yivan Zhang, Jindong Wang, Xing Xie, Masashi Sugiyama

To formally analyze this issue, we provide a unique algebraic formulation of the combination shift problem based on the concepts of homomorphism, equivariance, and a refined definition of disentanglement.

Disentanglement Domain Generalization

Approximating Instance-Dependent Noise via Instance-Confidence Embedding

1 code implementation25 Mar 2021 Yivan Zhang, Masashi Sugiyama

Label noise in multiclass classification is a major obstacle to the deployment of learning systems.

text-classification Text Classification

Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization

1 code implementation4 Feb 2021 Yivan Zhang, Gang Niu, Masashi Sugiyama

To estimate the transition matrix from noisy data, existing methods often need to estimate the noisy class-posterior, which could be unreliable due to the overconfidence of neural networks.

Weakly Supervised Classification

Classification with Rejection Based on Cost-sensitive Classification

no code implementations22 Oct 2020 Nontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang, Masashi Sugiyama

The goal of classification with rejection is to avoid risky misclassification in error-critical applications such as medical diagnosis and product inspection.

Classification General Classification +1

Learning from Aggregate Observations

1 code implementation NeurIPS 2020 Yivan Zhang, Nontawat Charoenphakdee, Zhenguo Wu, Masashi Sugiyama

We study the problem of learning from aggregate observations where supervision signals are given to sets of instances instead of individual instances, while the goal is still to predict labels of unseen individuals.

Classification General Classification +2

Learning from Indirect Observations

1 code implementation10 Oct 2019 Yivan Zhang, Nontawat Charoenphakdee, Masashi Sugiyama

Weakly-supervised learning is a paradigm for alleviating the scarcity of labeled data by leveraging lower-quality but larger-scale supervision signals.

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