Laurent El Ghaoui

Compressing High Capacity Models with Implicit Neural Networks and Frank-Wolfe

Reducing parameter footprint and inference latency of machine learning models is being driven by diverse applications like mobile vision and on-device intelligence [Choudary 20], and it is increasingly important, as models become increasingly large. In the proposed work, we will develop an alternative to the current train/compress paradigm, and instead we will train sparse high-capacity models from scratch, simultaneously achieving low training cost and high sparsity. We will also explore the robustness of such models.

To do so, we are building an optimization...

Adding Safety and Robustness to Learning for Robots by Learning on Robots

Safety and robustness of robotic systems are crucial for deploying robots in the real world. Machine learning has emerged as a promising tool for enabling robots to perform complex tasks or operate under uncertainties in dynamics and environment. However, learning techniques used often do not take safety into account, which could damage the robot or its environment, hindering deployment of learning-based methods. In contrast, in control theory, several methods such as model-predictive control, Hamilton-Jacobi (HJ) reachability, control barrier functions, and Lyapunov-...