Search Results for author:
Found 5 papers, 4 papers with code
From Learning to Optimize to Learning Optimization Algorithms
Towards designing learned optimization algorithms that are usable beyond their training setting, we identify key principles that classical algorithms obey, but have up to now, not been used for Learning to Optimize (L2O).
Near-optimal Closed-loop Method via Lyapunov Damping for Convex Optimization
We introduce an autonomous system with closed-loop damping for first-order convex optimization.
Inertial Newton Algorithms Avoiding Strict Saddle Points
We study the asymptotic behavior of second-order algorithms mixing Newton's method and inertial gradient descent in non-convex landscapes.
Second-order step-size tuning of SGD for non-convex optimization
In view of a direct and simple improvement of vanilla SGD, this paper presents a fine-tuning of its step-sizes in the mini-batch case.
An Inertial Newton Algorithm for Deep Learning
We prove the convergence of INNA for most deep learning problems.
