We theoretically establish that our algorithms almost surely converge to locally optimal policies of our safe optimization framework.

This paper examines learning the optimal filtering policy, known as the Kalman gain, for a linear system with unknown noise covariance matrices using noisy output data. The learning problem is formulated as a stochastic policy optimization problem, aiming to minimize the output prediction error. This formulation provides a direct bridge between data-driven optimal control and, its dual, optimal filtering.

A probabilistic classifier is considered "well calibrated" when its predicted probabilities closely align with the empirical frequencies of the corresponding labels [

However, current evidence indicates the contrary.

In the realm of artificial intelligence, continual learning has become an area of significant interest.

This makes it easily extensible and much more expressive than existing toolkits. It supports all 9 and all 10 of the decision-based group metrics of two popular review articles.

Geometric deep learning enables the encoding of physical symmetries in modeling 3D objects.

We establish a mathematical framework for guided diffusion to systematically study its optimization theory and algorithmic design.