Much remains to be understood, however, in statistical learning under distribution shifts. This paper focuses on a distribution shift setting where train and test distributions can be related by classes of (data) transformation maps.

In this paper, we formalize the active set ordering problem, which involves actively discovering a set of inputs based on their orderings determined by expensive evaluations of a blackbox function.

Existing VLN models are optimized through expert demonstrations by supervised behavioural cloning or incorporating manual reward engineering.

Offline reinforcement learning (RL) aims to learn a policy from a fixed dataset without additional interactions with the environment.

The web has become the primary communal source of data, or "data commons", for general-purpose

Heterogeneity, e.g., due to different types of layers or multiple sub-models, poses

Such approaches allow fast rendering and extracting each element of such a dynamic scene. However, modifying such objects over time is challenging.

In machine learning, model calibration and predictive inference are essential for producing reliable predictions and quantifying uncertainty to support decision-making.