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
21 papers
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…
Fairness in Selection Problems with Strategic Candidates
Vitalii Emelianov, Nicolas Gast, Patrick Loiseau
To better understand discriminations and the effect of affirmative actions in selection problems (e.g., college admission or hiring), a recent line of research proposed a model bas…
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…
A Random Matrix Perspective on Random Tensors
José Henrique de Morais Goulart, Romain Couillet, Pierre Comon
Tensor models play an increasingly prominent role in many fields, notably in machine learning. In several applications, such as community detection, topic modeling and Gaussian mix…
Regret minimization in stochastic non-convex learning via a proximal-gradient approach
Nadav Hallak, Panayotis Mertikopoulos, Volkan Cevher
Motivated by applications in machine learning and operations research, we study regret minimization with stochastic first-order oracle feedback in online constrained, and possibly…
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…
Fast Optimization with Zeroth-Order Feedback in Distributed, Multi-User MIMO Systems
Olivier Bilenne, Panayotis Mertikopoulos, E. Veronica Belmega
In this paper, we develop a gradient-free optimization methodology for efficient resource allocation in Gaussian MIMO multiple access channels. Our approach combines two main ingre…
A new regret analysis for Adam-type algorithms
Ahmet Alacaoglu, Yura Malitsky, Panayotis Mertikopoulos +1
In this paper, we focus on a theory-practice gap for Adam and its variants (AMSgrad, AdamNC, etc.). In practice, these algorithms are used with a constant first-order moment parame…
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…
Approximate Equilibria in Generalized Colonel Blotto and Generalized Lottery Blotto Games
Dong Quan Vu, Patrick Loiseau, Alonso Silva
In the Colonel Blotto game, two players with a fixed budget simultaneously allocate their resources across n battlefields to maximize the aggregate value gained from the battlefiel…
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…
The Price of Local Fairness in Multistage Selection
Vitalii Emelianov, George Arvanitakis, Nicolas Gast +2
The rise of algorithmic decision making led to active researches on how to define and guarantee fairness, mostly focusing on one-shot decision making. In several important applicat…
Path Planning Problems with Side Observations-When Colonels Play Hide-and-Seek
Dong Quan Vu, Patrick Loiseau, Alonso Silva +1
Resource allocation games such as the famous Colonel Blotto (CB) and Hide-and-Seek (HS) games are often used to model a large variety of practical problems, but only in their one-s…
Bandit learning in concave -person games
Mario Bravo, David S. Leslie, Panayotis Mertikopoulos
This paper examines the long-run behavior of learning with bandit feedback in non-cooperative concave games. The bandit framework accounts for extremely low-information environment…
Multi-agent online learning in time-varying games
Benoit Duvocelle, Panayotis Mertikopoulos, Mathias Staudigl +1
We examine the long-run behavior of multi-agent online learning in games that evolve over time. Specifically, we focus on a wide class of policies based on mirror descent, and we s…
Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile
Panayotis Mertikopoulos, Bruno Lecouat, Houssam Zenati +3
Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond. By necess…
Dispatching to Parallel Servers: Solutions of Poisson's Equation for First-Policy Improvement
Olivier Bilenne
Policy iteration techniques for multiple-server dispatching rely on the computation of value functions. In this context, we consider the continuous-space M/G/1-FCFS queue endowed w…
Power-of--Choices with Memory: Fluid Limit and Optimality
Jonatha Anselmi, Francois Dufour
In multi-server distributed queueing systems, the access of stochastically arriving jobs to resources is often regulated by a dispatcher, also known as load balancer. A fundamental…
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…
Distributed stochastic optimization via matrix exponential learning
Panayotis Mertikopoulos, E. Veronica Belmega, Romain Negrel +1
In this paper, we investigate a distributed learning scheme for a broad class of stochastic optimization problems and games that arise in signal processing and wireless communicati…
Boltzmann meets Nash: Energy-efficient routing in optical networks under uncertainty
Panayotis Mertikopoulos, Aris L. Moustakas, Anna Tzanakaki
Motivated by the massive deployment of power-hungry data centers for service provisioning, we examine the problem of routing in optical networks with the aim of minimizing traffic-…