This paper studies the problem of training a two-layer ReLU network for
...
The study of theoretical conditions for recovering sparse signals from
c...
In constrained reinforcement learning (C-RL), an agent seeks to learn fr...
We propose a structure-preserving model-reduction methodology for large-...
We consider the problem of learning an inner approximation of the region...
Motion planning methods for autonomous systems based on nonlinear progra...
In this work we address the problem of finding feasible policies for
Con...
This paper aims to put forward the concept that learning to take safe ac...
Neural networks trained via gradient descent with random initialization ...
We consider the problem of finding optimal policies for a Markov Decisio...
This paper aims to put forward the concept that learning to take safe ac...
We propose a new framework for studying the exact recovery of signals wi...
The ability to achieve coordinated behavior -- engineered or emergent --...
Motivated by the application of energy storage management in electricity...
Classical results in sparse recovery guarantee the exact reconstruction ...