Geoffrey Negiar — accepted AI-conference papers

2020

Linearly Convergent Frank-Wolfe with Backtracking Line-Search

Fabian Pedregosa, Geoffrey Negiar, Armin Askari, Martin Jaggi

AISTATS 2020poster

Structured constraints in Machine Learning have recently brought the Frank-Wolfe (FW) family of algorithms back in the spotlight. While the classical FW algorithm has poor local convergence properties, the Away-steps and Pairwise FW variants have emerged as improved variants with faster convergence.…

Cited by 78 Source ↗PDF ↗code ↗Scholar ↗

2020

Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization

Geoffrey Negiar, Gideon Dresdner, Alicia Tsai, Laurent El Ghaoui, Francesco Locatello, Robert Freund +1

ICML 2020poster

We propose a novel Stochastic Frank-Wolfe (a. k. a. conditional gradient) algorithm for constrained smooth finite-sum minimization with a generalized linear prediction/structure. This class of problems includes empirical risk minimization with sparse, low-rank, or other structured constraints. The p…

Cited by 41 Source ↗PDF ↗code ↗Scholar ↗