978 citations
- École Normale Supérieure - PSLFR306 papers
- Centre National de la Recherche ScientifiqueFR135 papers
- Institut national de recherche en sciences et technologies du numériqueFR94 papers
- Université Paris Sciences et LettresFR89 papers
- Université Paris CitéFR23 papers
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- SIERRA: Apprentissage Statistique et optimisationFR17 papers
- Université de MontréalCA15 papers
- École Polytechnique Fédérale de LausanneCH14 papers
- Czech Technical University in PragueCZ13 papers
- École PolytechniqueFR13 papers
- University of EdinburghGB13 papers
4 papers · 2 filters
Optimal Complexity and Certification of Bregman First-Order Methods
Radu-Alexandru Dragomir, Adrien Taylor, Alexandre d'Aspremont +1
We provide a lower bound showing that the convergence rate of the NoLips method (a.k.a. Bregman Gradient) is optimal for the class of functions satisfying the -smoothne…
Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks
Alireza Fallah, Mert Gurbuzbalaban, Asuman Ozdaglar +2
We study distributed stochastic gradient (D-SG) method and its accelerated variant (D-ASG) for solving decentralized strongly convex stochastic optimization problems where the obje…
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses
Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi
In this paper, we study large-scale convex optimization algorithms based on the Newton method applied to regularized generalized self-concordant losses, which include logistic regr…
Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions
Adrien Taylor, Francis Bach
We provide a novel computer-assisted technique for systematically analyzing first-order methods for optimization. In contrast with previous works, the approach is particularly suit…