How can the stochastic properties of input data and labels be appropriately captured to handle severe perturbations? To answer this question, we represent both input data and target labels as probability measures (i.e., probability densities), denoted asµn and ˆνn, respectively, in the Wasserstein space and solve a distance-based classification problem (i.e.,

Generative AI and large language models hold great promise in enhancing programming education by generating individualized feedback and hints for learners. Recent works have primarily focused on improving the quality of generated feedback to achieve human tutors' quality.

Inthiswork, we carefully study the performance of PPO in cooperative multi-agent settings.

The scarcity of labeled dataset leads us to consider the integration of data augmentation and active learning.

Still, many relevant tasks such as object manipulation pose significant challenges forRL.