2 citations · 3 across the 6 of their papers we have counts for
6 papers
A Direction Adaptation Evaluation Strategy for Noisy Derivative-Free Optimization
Morteza Kimiaei, Mahsa Yousefi
In this paper, we develop a direction adaptation evolution strategy (DAES) -- a new MAES-type method -- for noisy derivative-free optimization, designed to reconcile the population…
A Projected Stochastic Gradient Method for Finite-Sum Problems with Linear Equality Constraints
Natasa Krklec Jerinkic, Benedetta Morini, Mahsa Yousefi
A stochastic gradient method for finite-sum minimization subject to deterministic linear constraints is proposed and analyzed. The procedure presented adapts the projected gradient…
Fully stochastic trust-region methods with Barzilai-Borwein steplengths
Stefania Bellavia, Benedetta Morini, Mahsa Yousefi
We investigate stochastic gradient methods and stochastic counterparts of the Barzilai-Borwein steplengths and their application to finite-sum minimization problems. Our proposal i…
Majorization-Minimization for sparse SVMs
Alessandro Benfenati, Emilie Chouzenoux, Giorgia Franchini +5
Several decades ago, Support Vector Machines (SVMs) were introduced for performing binary classification tasks, under a supervised framework. Nowadays, they often outperform other…
A non-monotone trust-region method with noisy oracles and additional sampling
Natasa Krejic, Natasa Krklec Jerinkic, Angeles Martinez +1
In this work, we introduce a novel stochastic second-order method, within the framework of a non-monotone trust-region approach, for solving the unconstrained, nonlinear, and non-c…
On the efficiency of Stochastic Quasi-Newton Methods for Deep Learning
Mahsa Yousefi, Angeles Martinez
While first-order methods are popular for solving optimization problems that arise in large-scale deep learning problems, they come with some acute deficiencies. To diminish such s…