The trustworthiness of machine learning has emerged as a critical topic in the field,
encompassing various applications and research areas such as robustness, security …

Abstract Gradual Domain Adaptation (GDA), in which the learner is provided with additional
intermediate domains, has been theoretically and empirically studied in many contexts …

Abstract Existing Source-Free Domain Adaptation (SFDA) methods typically adopt the
feature distribution alignment paradigm via mining auxiliary information (eg., pseudo …

Abstract Curriculum Reinforcement Learning (CRL) aims to create a sequence of tasks,
starting from easy ones and gradually learning towards difficult tasks. In this work, we focus …

Unsupervised domain adaptation (UDA) adapts a model from a labeled source domain to an
unlabeled target domain in a one-off way. Though widely applied, UDA faces a great …

Domain shift degrades classification models on new data distributions. Conventional
unsupervised domain adaptation (UDA) aims to learn features that bridge labeled source …

We establish a new model-agnostic optimization framework for out-of-distribution
generalization via multicalibration, a criterion that ensures a predictor is calibrated across a …

Deep learning has been increasingly employed in data-driven structural health monitoring
(SHM), forming a new paradigm of assessing structural conditions and identifying structural …

Given the limited availability of accurately labeled data in fault diagnosis across various
industrial scenarios, we proposed a Gradual Conditional Domain Adversarial Network …

In the field of domain generalization, the task of constructing a predictive model capable of
generalizing to a target domain without access to target data remains challenging. This …