114 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Laboratoire d'Informatique de GrenobleFR18 papers
- Centre National de la Recherche ScientifiqueFR9 papers
- Criteo (France)FR6 papers
- Institut polytechnique de GrenobleFR6 papers
- Centre Inria de l'Université Grenoble AlpesFR5 papers
- Université Grenoble AlpesFR5 papers
- Institut national de recherche en sciences et technologies du numériqueFR4 papers
- École Polytechnique Fédérale de LausanneCH3 papers
- École Nationale Supérieure de l'Électronique et de ses ApplicationsFR2 papers
- École Normale Supérieure - PSLFR2 papers
- Equipes Traitement de l'Information et SystèmesFR2 papers
- Institut Polytechnique de BordeauxFR2 papers
6 papers · 1 filter
Push--Pull with Device Sampling
Yu-Guan Hsieh, Yassine Laguel, Franck Iutzeler +1
We consider decentralized optimization problems in which a number of agents collaborate to minimize the average of their local functions by exchanging over an underlying communicat…
Asynchronous Load Balancing and Auto-scaling: Mean-Field Limit and Optimal Design
Jonatha Anselmi
We develop a Markovian framework for load balancing that combines classical algorithms such as Power-of- with auto-scaling mechanisms that allow the net service capacity to scal…
The limits of min-max optimization algorithms: convergence to spurious non-critical sets
Ya-Ping Hsieh, Panayotis Mertikopoulos, Volkan Cevher
Compared to ordinary function minimization problems, min-max optimization algorithms encounter far greater challenges because of the existence of periodic cycles and similar phenom…
Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling
Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick +1
Owing to their stability and convergence speed, extragradient methods have become a staple for solving large-scale saddle-point problems in machine learning. The basic premise of t…
On the convergence of single-call stochastic extra-gradient methods
Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick +1
Variational inequalities have recently attracted considerable interest in machine learning as a flexible paradigm for models that go beyond ordinary loss function minimization (suc…
On the convergence of gradient-like flows with noisy gradient input
Panayotis Mertikopoulos, Mathias Staudigl
In view of solving convex optimization problems with noisy gradient input, we analyze the asymptotic behavior of gradient-like flows under stochastic disturbances. Specifically, we…