Search Results for author: Zhenghang Cui

Found 7 papers, 2 papers with code

ATRO: Adversarial Training with a Rejection Option

no code implementations24 Oct 2020 Masahiro Kato, Zhenghang Cui, Yoshihiro Fukuhara

In this paper, in order to acquire a more reliable classifier against adversarial attacks, we propose the method of Adversarial Training with a Rejection Option (ATRO).

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

Active Classification with Uncertainty Comparison Queries

1 code implementation3 Aug 2020 Zhenghang Cui, Issei Sato

We then propose an efficient adaptive labeling algorithm using the proposed oracle and the positivity comparison oracle.

Active Learning Classification +1

Classification from Triplet Comparison Data

1 code implementation24 Jul 2019 Zhenghang Cui, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama

Although learning from triplet comparison data has been considered in many applications, an important fundamental question of whether we can learn a classifier only from triplet comparison data has remained unanswered.

Classification General Classification +1

Variational Domain Adaptation

no code implementations ICLR 2019 Hirono Okamoto, Shohei Ohsawa, Itto Higuchi, Haruka Murakami, Mizuki Sango, Zhenghang Cui, Masahiro Suzuki, Hiroshi Kajino, Yutaka Matsuo

It reformulates the posterior with a natural paring $\langle, \rangle: \mathcal{Z} \times \mathcal{Z}^* \rightarrow \Real$, which can be expanded to uncountable infinite domains such as continuous domains as well as interpolation.

Bayesian Inference Domain Adaptation +2

Stochastic Divergence Minimization for Biterm Topic Model

no code implementations1 May 2017 Zhenghang Cui, Issei Sato, Masashi Sugiyama

As the emergence and the thriving development of social networks, a huge number of short texts are accumulated and need to be processed.

Topic Models Variational Inference

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