450 citations · 593 across the 25 of their papers we have counts for
11 papers · 1 filter
A Symmetric Loss Perspective of Reliable Machine Learning
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 o…
On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective
Nontawat Charoenphakdee, Jayakorn Vongkulbhisal, Nuttapong Chairatanakul +1
The focal loss has demonstrated its effectiveness in many real-world applications such as object detection and image classification, but its theoretical understanding has been limi…
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…
Robust Imitation Learning from Noisy Demonstrations
Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama
Robust learning from noisy demonstrations is a practical but highly challenging problem in imitation learning. In this paper, we first theoretically show that robust imitation lear…
Learning from Aggregate Observations
Yivan Zhang, Nontawat Charoenphakdee, Zhenguo Wu +1
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 pr…
Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time
Hideaki Imamura, Nontawat Charoenphakdee, Futoshi Futami +3
The Gaussian process bandit is a problem in which we want to find a maximizer of a black-box function with the minimum number of function evaluations. If the black-box function var…
Learning from Indirect Observations
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. While existing work mainly f…
On the Calibration of Multiclass Classification with Rejection
Chenri Ni, Nontawat Charoenphakdee, Junya Honda +1
We investigate the problem of multiclass classification with rejection, where a classifier can choose not to make a prediction to avoid critical misclassification. First, we consid…
Domain Discrepancy Measure for Complex Models in Unsupervised Domain Adaptation
Jongyeong Lee, Nontawat Charoenphakdee, Seiichi Kuroki +1
Appropriately evaluating the discrepancy between domains is essential for the success of unsupervised domain adaptation. In this paper, we first point out that existing discrepancy…
On Symmetric Losses for Learning from Corrupted Labels
Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama
This paper aims to provide a better understanding of a symmetric loss. First, we emphasize that using a symmetric loss is advantageous in the balanced error rate (BER) minimization…
Positive-Unlabeled Classification under Class Prior Shift and Asymmetric Error
Nontawat Charoenphakdee, Masashi Sugiyama
Bottlenecks of binary classification from positive and unlabeled data (PU classification) are the requirements that given unlabeled patterns are drawn from the test marginal distri…