We investigate the problem of corruption robustness in offline reinforcement learning (RL)
with general function approximation, where an adversary can corrupt each sample in the …

Large language models (LLMs) often exhibit deficient reasoning or generate hallucinations.
To address these, studies prefixed with" Self-" such as Self-Consistency, Self-Improve, and …

Protein engineering through directed evolution and (semi) rational design has become a
powerful approach for optimizing and enhancing proteins with desired properties. The …

Reliable uncertainty estimation has become a crucial requirement for the industrial
deployment of deep learning algorithms, particularly in high-risk applications such as …

Uncertainty estimation aims to evaluate the confidence of a trained deep neural network.
However, existing uncertainty estimation approaches rely on low-dimensional distributional …

Multi-view learning methods often focus on improving decision accuracy, while neglecting
the decision uncertainty, limiting their suitability for safety-critical applications. To mitigate …

Abstract Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) plays a pivotal
role in civilian and military applications. However, the limited labeled samples present a …

In Open-Set Domain Generalization (OSDG), the model is exposed to both new variations of
data appearance (domains) and open-set conditions, where both known and novel …

Sequential recommendation systems suggest products based on users' historical
behaviours. The inherent sparsity of user-item interactions in a vast product space often …

The application of deep learning techniques to analyze brain functional magnetic resonance
imaging (fMRI) data has led to significant advancements in identifying prospective …