Search Results for author: Nontawat Charoenphakdee

Found 18 papers, 7 papers with code

Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification

no code implementations1 Feb 2022 Takashi Ishida, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu, Masashi Sugiyama

There is a fundamental limitation in the prediction performance that a machine learning model can achieve due to the inevitable uncertainty of the prediction target.

Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous Graph

1 code implementation Findings (EMNLP) 2021 Nuttapong Chairatanakul, Noppayut Sriwatanasakdi, Nontawat Charoenphakdee, Xin Liu, Tsuyoshi Murata

To address this challenge, we propose dictionary-based heterogeneous graph neural network (DHGNet) that effectively handles the heterogeneity of DHG by two-step aggregations, which are word-level and language-level aggregations.

Cross-Lingual Transfer text-classification +2

A Symmetric Loss Perspective of Reliable Machine Learning

no code implementations5 Jan 2021 Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama

When minimizing the empirical risk in binary classification, it is a common practice to replace the zero-one loss with a surrogate loss to make the learning objective feasible to optimize.

BIG-bench Machine Learning General Classification +1

On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective

no code implementations CVPR 2021 Nontawat Charoenphakdee, Jayakorn Vongkulbhisal, Nuttapong Chairatanakul, Masashi Sugiyama

In this paper, we first prove that the focal loss is classification-calibrated, i. e., its minimizer surely yields the Bayes-optimal classifier and thus the use of the focal loss in classification can be theoretically justified.

Classification General Classification +3

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

Robust Imitation Learning from Noisy Demonstrations

1 code implementation20 Oct 2020 Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama

Robust learning from noisy demonstrations is a practical but highly challenging problem in imitation learning.

Classification Continuous Control +2

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 +1

Learning Only from Relevant Keywords and Unlabeled Documents

no code implementations IJCNLP 2019 Nontawat Charoenphakdee, Jongyeong Lee, Yiping Jin, Dittaya Wanvarie, Masashi Sugiyama

We consider a document classification problem where document labels are absent but only relevant keywords of a target class and unlabeled documents are given.

Document Classification General Classification +1

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.

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

Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization

no code implementations31 Jan 2019 Taira Tsuchiya, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama

We further provide an estimation error bound to show that our risk estimator is consistent.

On the Calibration of Multiclass Classification with Rejection

1 code implementation NeurIPS 2019 Chenri Ni, Nontawat Charoenphakdee, Junya Honda, Masashi Sugiyama

First, we consider an approach based on simultaneous training of a classifier and a rejector, which achieves the state-of-the-art performance in the binary case.

Classification General Classification

Positive-Unlabeled Classification under Class Prior Shift and Asymmetric Error

no code implementations19 Sep 2018 Nontawat Charoenphakdee, Masashi Sugiyama

Based on the analysis of the Bayes optimal classifier, we show that given a test class prior, PU classification under class prior shift is equivalent to PU classification with asymmetric error.

Classification Density Ratio Estimation +1

Unsupervised Domain Adaptation Based on Source-guided Discrepancy

no code implementations11 Sep 2018 Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama

A previously proposed discrepancy that does not use the source domain labels requires high computational cost to estimate and may lead to a loose generalization error bound in the target domain.

Unsupervised Domain Adaptation

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