284 citations · 405 across the 19 of their papers we have counts for
8 papers · 1 filter
On the Theoretical Properties of Noise Correlation in Stochastic Optimization
Aurelien Lucchi, Frank Proske, Antonio Orvieto +2
Studying the properties of stochastic noise to optimize complex non-convex functions has been an active area of research in the field of machine learning. Prior work has shown that…
On the Second-order Convergence Properties of Random Search Methods
Aurelien Lucchi, Antonio Orvieto, Adamos Solomou
We study the theoretical convergence properties of random-search methods when optimizing non-convex objective functions without having access to derivatives. We prove that standard…
Direct-Search for a Class of Stochastic Min-Max Problems
Sotiris Anagnostidis, Aurelien Lucchi, Youssef Diouane
Recent applications in machine learning have renewed the interest of the community in min-max optimization problems. While gradient-based optimization methods are widely used to so…
An Accelerated DFO Algorithm for Finite-sum Convex Functions
Yuwen Chen, Antonio Orvieto, Aurelien Lucchi
Derivative-free optimization (DFO) has recently gained a lot of momentum in machine learning, spawning interest in the community to design faster methods for problems where gradien…
Momentum Improves Optimization on Riemannian Manifolds
Foivos Alimisis, Antonio Orvieto, Gary Bécigneul +1
We develop a new Riemannian descent algorithm that relies on momentum to improve over existing first-order methods for geodesically convex optimization. In contrast, accelerated co…
Shadowing Properties of Optimization Algorithms
Antonio Orvieto, Aurelien Lucchi
Ordinary differential equation (ODE) models of gradient-based optimization methods can provide insights into the dynamics of learning and inspire the design of new algorithms. Unfo…