Statistical Learning
CONTRAST: Continual Multi-source Adaptation to Dynamic Distributions
Adapting to dynamic data distributions is a practical yet challenging task.
Average gradient outer product as a mechanism for deep neural collapse
This assumes that DNNs are infinitely expressive and, thus, optimizes for the feature vectors in the last layer directly.
Probing the Decision Boundaries of In-context Learning in Large Language Models
Recent language models, such as GPT -3+ [Brown et al., 2020, Achiam et al., 2023], have demonstrated Recent attempts to understand in-context learning have focused on various aspects. On the practical side, research has investigated the impact of different factors on in-context learning.
Addressing Hidden Confounding with Heterogeneous Observational Datasets for Recommendation
The collected data in recommender systems generally suffers selection bias. Considerable works are proposed to address selection bias induced by observed user and item features, but they fail when hidden features (e.g., user age or salary) that affect
