7 accepted papers
Large language models (LLMs) are widely used for conversational systems, but they face significant challenges in interpretability of dialogue flow and reproducibility of expert knowledge. To address this, we propose a novel method that extracts flowcharts from dialogue data and incorporates them int…
Positive and unlabeled (PU) learning is a fundamental task in many applications, which trains a binary classifier from only PU data. Existing PU learning methods typically assume that training and test distributions are identical. However, this assumption is often violated due to distribution shifts…
Diffusion models are powerful generative models but often generate sensitive data that are unwanted by users, mainly because the unlabeled training data frequently contain such sensitive data. Since labeling all sensitive data in the large-scale unlabeled training data is impractical, we address thi…
Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced binary classification tasks. Existing AUC maximization methods typically assume that training and test distributions are identical. However, this assumption is often violated due to {\it…
Maximizing the area under the receiver operating characteristic curve (AUC) is a popular approach to imbalanced binary classification problems. Existing AUC maximization methods usually assume that training and test distributions are identical. However, this assumption is often violated in practice…
We propose a meta-learning method to improve the anomaly detection performance on unseen target tasks that have only unlabeled data. Existing meta-learning methods for anomaly detection have shown remarkable performance but require labeled data in target tasks. Although they can treat unlabeled data…
This paper proposes a new loss function for adversarial training. Since adversarial training has difficulties, e.g., necessity of high model capacity, focusing on important data points by weighting cross-entropy loss has attracted much attention. However, they are vulnerable to sophisticated attacks…