5 papers
ATRO: Adversarial Training with a Rejection Option
Masahiro Kato, Zhenghang Cui, Yoshihiro Fukuhara
This paper proposes a classification framework with a rejection option to mitigate the performance deterioration caused by adversarial examples. While recent machine learning algor…
Classification with Rejection Based on Cost-sensitive Classification
Nontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang +1
The goal of classification with rejection is to avoid risky misclassification in error-critical applications such as medical diagnosis and product inspection. In this paper, based…
Active Classification with Uncertainty Comparison Queries
Zhenghang Cui, Issei Sato
Noisy pairwise comparison feedback has been incorporated to improve the overall query complexity of interactively learning binary classifiers. The \textit{positivity comparison ora…
Classification from Triplet Comparison Data
Zhenghang Cui, Nontawat Charoenphakdee, Issei Sato +1
Learning from triplet comparison data has been extensively studied in the context of metric learning, where we want to learn a distance metric between two instances, and ordinal em…
Bayesian posterior approximation via greedy particle optimization
Futoshi Futami, Zhenghang Cui, Issei Sato +1
In Bayesian inference, the posterior distributions are difficult to obtain analytically for complex models such as neural networks. Variational inference usually uses a parametric…