Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

The success of reinforcement learning in a variety of challenging sequential decision-
making problems has been much discussed, but often ignored in this discussion is the …

Subpopulation shift widely exists in many real-world machine learning applications, referring
to the training and test distributions containing the same subpopulation groups but varying in …

Pre-trained text-to-image generative models can produce diverse, semantically rich, and
realistic images from natural language descriptions. Compared with language, images …

Psychological curiosity plays a significant role in human intelligence to enhance learning
through exploration and information acquisition. In the Artificial Intelligence (AI) community …

Complex and contact-rich robotic manipulation tasks, particularly those that involve multi-
fingered hands and underactuated object manipulation, present a significant challenge to …

In recent years, significant progress has been made in the field of robotic reinforcement
learning (RL), enabling methods that handle complex image observations, train in the real …

Abstract Recent curriculum Reinforcement Learning (RL) has shown notable progress in
solving complex tasks by proposing sequences of surrogate tasks. However, the previous …

Fast-Rate PAC-Bayesian Generalization Bounds for Meta-Learning Page 1 Background
Motivation Improved PAC-Bayesian Bounds Algorithms and Experiments Conclusions …