Panayotis Mertikopoulos

Orcid: 0000-0003-2026-9616

Affiliations:
  • Université Grenoble Alpes, Grenoble INP, LIG, France


According to our database1, Panayotis Mertikopoulos authored at least 160 papers between 2007 and 2024.

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Bibliography

2024
A unified stochastic approximation framework for learning in games.
Math. Program., January, 2024

The Rate of Convergence of Bregman Proximal Methods: Local Geometry Versus Regularity Versus Sharpness.
SIAM J. Optim., 2024

Nested replicator dynamics, nested logit choice, and similarity-based learning.
J. Econ. Theory, 2024

Tamed Langevin sampling under weaker conditions.
CoRR, 2024

On the discrete-time origins of the replicator dynamics: From convergence to instability and chaos.
CoRR, 2024

A Geometric Decomposition of Finite Games: Convergence vs. Recurrence under Exponential Weights.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

The Computational Complexity of Finding Second-Order Stationary Points.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

What is the Long-Run Distribution of Stochastic Gradient Descent? A Large Deviations Analysis.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Multiagent Online Learning in Time-Varying Games.
Math. Oper. Res., May, 2023

Optimization Challenges in Data Science - Special Issue Editorial.
EURO J. Comput. Optim., January, 2023

A Quadratic Speedup in Finding Nash Equilibria of Quantum Zero-Sum Games.
CoRR, 2023

Learning in quantum games.
CoRR, 2023

Exploiting hidden structures in non-convex games for convergence to Nash equilibrium.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Payoff-based Learning with Matrix Multiplicative Weights in Quantum Games.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Riemannian stochastic optimization methods avoid strict saddle points.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

The Equivalence of Dynamic and Strategic Stability under Regularized Learning in Games.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

The Stability of Matrix Multiplicative Weights Dynamics in Quantum Games.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

2022
Online Reconfiguration of IoT Applications in the Fog: The Information-Coordination Trade-Off.
IEEE Trans. Parallel Distributed Syst., 2022

Distributed Stochastic Optimization with Large Delays.
Math. Oper. Res., 2022

Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism.
J. Mach. Learn. Res., 2022

On the rate of convergence of Bregman proximal methods in constrained variational inequalities.
CoRR, 2022

Explicit Second-Order Min-Max Optimization Methods with Optimal Convergence Guarantee.
CoRR, 2022

Survival of dominated strategies under imitation dynamics.
CoRR, 2022

Learning in games from a stochastic approximation viewpoint.
CoRR, 2022

Routing in an Uncertain World: Adaptivity, Efficiency, and Equilibrium.
CoRR, 2022

Online convex optimization in wireless networks and beyond: The feedback-performance trade-off.
Proceedings of the 20th International Symposium on Modeling and Optimization in Mobile, 2022

No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On the convergence of policy gradient methods to Nash equilibria in general stochastic games.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Nested Bandits.
Proceedings of the International Conference on Machine Learning, 2022

UnderGrad: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees.
Proceedings of the International Conference on Machine Learning, 2022

AdaGrad Avoids Saddle Points.
Proceedings of the International Conference on Machine Learning, 2022

The Dynamics of Riemannian Robbins-Monro Algorithms.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Learning in Games with Quantized Payoff Observations.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Pick your Neighbor: Local Gauss-Southwell Rule for Fast Asynchronous Decentralized Optimization.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Asymptotic Degradation of Linear Regression Estimates with Strategic Data Sources.
Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

2021
Robust Power Management via Learning and Game Design.
Oper. Res., 2021

A heuristic for estimating Nash equilibria in first-price auctions with correlated values.
CoRR, 2021

Adaptive first-order methods revisited: Convex optimization without Lipschitz requirements.
CoRR, 2021

Learning in nonatomic games, Part I: Finite action spaces and population games.
CoRR, 2021

From Learning with Partial Information to Bandits: Only Strict Nash Equilibria are Stable.
CoRR, 2021

Fast Routing under Uncertainty: Adaptive Learning in Congestion Games via Exponential Weights.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

The convergence rate of regularized learning in games: From bandits and uncertainty to optimism and beyond.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Sifting through the noise: Universal first-order methods for stochastic variational inequalities.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Adaptive First-Order Methods Revisited: Convex Minimization without Lipschitz Requirements.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

The Limits of Min-Max Optimization Algorithms: Convergence to Spurious Non-Critical Sets.
Proceedings of the 38th International Conference on Machine Learning, 2021

Zeroth-Order Non-Convex Learning via Hierarchical Dual Averaging.
Proceedings of the 38th International Conference on Machine Learning, 2021

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach.
Proceedings of the 38th International Conference on Machine Learning, 2021

Adaptive Extra-Gradient Methods for Min-Max Optimization and Games.
Proceedings of the 9th International Conference on Learning Representations, 2021

Adaptive Learning in Continuous Games: Optimal Regret Bounds and Convergence to Nash Equilibrium.
Proceedings of the Conference on Learning Theory, 2021

Survival of the strictest: Stable and unstable equilibria under regularized learning with partial information.
Proceedings of the Conference on Learning Theory, 2021

The Last-Iterate Convergence Rate of Optimistic Mirror Descent in Stochastic Variational Inequalities.
Proceedings of the Conference on Learning Theory, 2021

Equilibrium Tracking and Convergence in Dynamic Games.
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

Optimization in Open Networks via Dual Averaging.
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

2020
Fast Optimization With Zeroth-Order Feedback in Distributed, Multi-User MIMO Systems.
IEEE Trans. Signal Process., 2020

On the Convergence of Mirror Descent beyond Stochastic Convex Programming.
SIAM J. Optim., 2020

When Is Selfish Routing Bad? The Price of Anarchy in Light and Heavy Traffic.
Oper. Res., 2020

Gradient-free Online Learning in Games with Delayed Rewards.
CoRR, 2020

Fast Gradient-Free Optimization in Distributed Multi-User MIMO Systems.
CoRR, 2020

Quick or Cheap? Breaking Points in Dynamic Markets.
Proceedings of the EC '20: The 21st ACM Conference on Economics and Computation, 2020

No-Regret Learning and Mixed Nash Equilibria: They Do Not Mix.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Online Non-Convex Optimization with Imperfect Feedback.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Derivative-Free Optimization over Multi-user MIMO Networks.
Proceedings of the Network Games, Control and Optimization - 10th International Conference, 2020

Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games.
Proceedings of the 37th International Conference on Machine Learning, 2020

Gradient-free Online Learning in Continuous Games with Delayed Rewards.
Proceedings of the 37th International Conference on Machine Learning, 2020

A new regret analysis for Adam-type algorithms.
Proceedings of the 37th International Conference on Machine Learning, 2020

Online and stochastic optimization beyond Lipschitz continuity: A Riemannian approach.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
Online Power Optimization in Feedback-Limited, Dynamic and Unpredictable IoT Networks.
IEEE Trans. Signal Process., 2019

Hessian Barrier Algorithms for Linearly Constrained Optimization Problems.
SIAM J. Optim., 2019

Learning in games with continuous action sets and unknown payoff functions.
Math. Program., 2019

Forward-backward-forward methods with variance reduction for stochastic variational inequalities.
CoRR, 2019

Gradient-free Online Resource Allocation Algorithms for Dynamic Wireless Networks.
Proceedings of the 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications, 2019

On the convergence of single-call stochastic extra-gradient methods.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

An adaptive Mirror-Prox method for variational inequalities with singular operators.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Large-Scale Network Utility Maximization: Countering Exponential Growth with Exponentiated Gradients.
Proceedings of the 2019 IEEE Conference on Computer Communications, 2019

Cautious Regret Minimization: Online Optimization with Long-Term Budget Constraints.
Proceedings of the 36th International Conference on Machine Learning, 2019

Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile.
Proceedings of the 7th International Conference on Learning Representations, 2019

Load Aware Provisioning of IoT Services on Fog Computing Platform.
Proceedings of the 2019 IEEE International Conference on Communications, 2019

Fog Based Framework for IoT Service Provisioning.
Proceedings of the 16th IEEE Annual Consumer Communications & Networking Conference, 2019

Demo: Fog Based Framework for IoT Service Orchestration.
Proceedings of the 16th IEEE Annual Consumer Communications & Networking Conference, 2019

2018
On the Convergence of Gradient-Like Flows with Noisy Gradient Input.
SIAM J. Optim., 2018

Stochastic Mirror Descent Dynamics and Their Convergence in Monotone Variational Inequalities.
J. Optim. Theory Appl., 2018

Riemannian game dynamics.
J. Econ. Theory, 2018

Learning in time-varying games.
CoRR, 2018

Mirror descent in saddle-point problems: Going the extra (gradient) mile.
CoRR, 2018

Online convex optimization and no-regret learning: Algorithms, guarantees and applications.
CoRR, 2018

Cycles in Adversarial Regularized Learning.
Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, 2018

Learning in Games with Lossy Feedback.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Bandit Learning in Concave N-Person Games.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

A Resource Allocation Framework for Network Slicing.
Proceedings of the 2018 IEEE Conference on Computer Communications, 2018

Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?
Proceedings of the 35th International Conference on Machine Learning, 2018

Power Control with Random Delays: Robust Feedback Averaging.
Proceedings of the 57th IEEE Conference on Decision and Control, 2018

2017
Distributed Stochastic Optimization via Matrix Exponential Learning.
IEEE Trans. Signal Process., 2017

Semi-Cognitive Radio Networks: A Novel Dynamic Spectrum Sharing Mechanism.
IEEE Trans. Cogn. Commun. Netw., 2017

Mixed-Strategy Learning With Continuous Action Sets.
IEEE Trans. Autom. Control., 2017

On the robustness of learning in games with stochastically perturbed payoff observations.
Games Econ. Behav., 2017

Mirror descent in non-convex stochastic programming.
CoRR, 2017

On the asymptotic behavior of the price of anarchy: Is selfish routing bad in highly congested networks?
CoRR, 2017

Auction-based resource allocation in OpenFlow multi-tenant networks.
Comput. Networks, 2017

The Asymptotic Behavior of the Price of Anarchy.
Proceedings of the Web and Internet Economics - 13th International Conference, 2017

Hedging Under Uncertainty: Regret Minimization Meets Exponentially Fast Convergence.
Proceedings of the Algorithmic Game Theory - 10th International Symposium, 2017

Least action routing: Identifying the optimal path in a wireless relay network.
Proceedings of the 28th IEEE Annual International Symposium on Personal, 2017

Countering Feedback Delays in Multi-Agent Learning.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Stochastic Mirror Descent in Variationally Coherent Optimization Problems.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Learning with Bandit Feedback in Potential Games.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Stable Power Control in Wireless Networks via Dual Averaging.
Proceedings of the 2017 IEEE Global Communications Conference, 2017

Mirror descent learning in continuous games.
Proceedings of the 56th IEEE Annual Conference on Decision and Control, 2017

Convergence to nash equilibrium in continuous games with noisy first-order feedback.
Proceedings of the 56th IEEE Annual Conference on Decision and Control, 2017

2016
Learning in an Uncertain World: MIMO Covariance Matrix Optimization With Imperfect Feedback.
IEEE Trans. Signal Process., 2016

Power Optimization in Random Wireless Networks.
IEEE Trans. Inf. Theory, 2016

Learning in Games via Reinforcement and Regularization.
Math. Oper. Res., 2016

Learning to Be Green: Robust Energy Efficiency Maximization in Dynamic MIMO-OFDM Systems.
IEEE J. Sel. Areas Commun., 2016

Imitation dynamics with payoff shocks.
Int. J. Game Theory, 2016

Boltzmann meets Nash: Energy-efficient routing in optical networks under uncertainty.
CoRR, 2016

Learning in concave games with imperfect information.
CoRR, 2016

Exponentially fast convergence to (strict) equilibrium via hedging.
CoRR, 2016

Online Power Allocation for Opportunistic Radio Access in Dynamic OFDM Networks.
Proceedings of the IEEE 84th Vehicular Technology Conference, 2016

A novel dynamic network architecture model based on stochastic geometry and game theory.
Proceedings of the 2016 IEEE International Conference on Communications, 2016

Online Interference Mitigation via Learning in Dynamic IoT Environments.
Proceedings of the 2016 IEEE Globecom Workshops, Washington, DC, USA, December 4-8, 2016, 2016

Distributed learning for resource allocation under uncertainty.
Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing, 2016

2015
Interference-Based Pricing for Opportunistic Multicarrier Cognitive Radio Systems.
IEEE Trans. Wirel. Commun., 2015

Energy-Aware Competitive Power Allocation for Heterogeneous Networks Under QoS Constraints.
IEEE Trans. Wirel. Commun., 2015

Inertial Game Dynamics and Applications to Constrained Optimization.
SIAM J. Control. Optim., 2015

Penalty-Regulated Dynamics and Robust Learning Procedures in Games.
Math. Oper. Res., 2015

Adaptive Power Allocation and Control in Time-Varying Multi-Carrier MIMO Networks.
CoRR, 2015

In an Uncertain World: Distributed Optimization in MIMO Systems with Imperfect Information.
CoRR, 2015

A stochastic approximation algorithm for stochastic semidefinite programming.
CoRR, 2015

Cost-Efficient Throughput Maximization in Multi-Carrier Cognitive Radio Systems.
CoRR, 2015

No more tears: A no-regret approach to power control in dynamically varying MIMO networks.
Proceedings of the 13th International Symposium on Modeling and Optimization in Mobile, 2015

Energy-Efficient Power Allocation in Dynamic Multi-Carrier Systems.
Proceedings of the IEEE 81st Vehicular Technology Conference, 2015

2014
Transmit without Regrets: Online Optimization in MIMO-OFDM Cognitive Radio Systems.
IEEE J. Sel. Areas Commun., 2014

Game-theoretical control with continuous action sets.
CoRR, 2014

Regularized Best Responses and Reinforcement Learning in Games.
CoRR, 2014

A continuous-time approach to online optimization.
CoRR, 2014

Adaptive transmit policies for cost-efficient power allocation in multi-carrier systems.
Proceedings of the 12th International Symposium on Modeling and Optimization in Mobile, 2014

Energy-aware competitive link adaptation in small-cell networks.
Proceedings of the 12th International Symposium on Modeling and Optimization in Mobile, 2014

Distributed optimization in multi-user MIMO systems with imperfect and delayed information.
Proceedings of the 2014 IEEE International Symposium on Information Theory, Honolulu, HI, USA, June 29, 2014

No regrets: Distributed power control under time-varying channels and QoS requirements.
Proceedings of the 52nd Annual Allerton Conference on Communication, 2014

2013
Higher order game dynamics.
J. Econ. Theory, 2013

Entropy-driven dynamics and robust learning procedures in games
CoRR, 2013

Adaptive spectrum management in MIMO-OFDM cognitive radio: an exponential learning approach.
Proceedings of the 7th International Conference on Performance Evaluation Methodologies and Tools, 2013

Riemannian-geometric optimization methods for MIMO multiple access channels.
Proceedings of the 2013 IEEE International Symposium on Information Theory, 2013

Entropy-driven optimization dynamics for Gaussian vector multiple access channels.
Proceedings of the IEEE International Conference on Communications, 2013

Accelerating population-based search heuristics by adaptive resource allocation.
Proceedings of the Genetic and Evolutionary Computation Conference, 2013

2012
Distributed Learning Policies forPower Allocation in Multiple Access Channels.
IEEE J. Sel. Areas Commun., 2012

Strange bedfellows: Riemann, gibbs and vector Gaussian multiple access channels.
Proceedings of the 6th International Conference on Network Games, 2012

Matrix exponential learning: Distributed optimization in MIMO systems.
Proceedings of the 2012 IEEE International Symposium on Information Theory, 2012

2011
Living at the Edge: A Large Deviations Approach to the Outage MIMO Capacity.
IEEE Trans. Inf. Theory, 2011

Distributed Learning Policies for Power Allocation in Multiple Access Channels
CoRR, 2011

Selfish routing revisited: degeneracy, evolution and stochastic fluctuations.
Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools Communications, 2011

Dynamic power allocation games in parallel multiple access channels.
Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools Communications, 2011

2010
Balancing traffic in networks: redundancy, learning, and the effect of stochastic fluctuations.
Proceedings of the Behavioral and Quantitative Game Theory, 2010

2009
Rational Behaviour in the Presence of Stochastic Perturbations
CoRR, 2009

Distribution of MIMO mutual information: A large deviations approach.
Proceedings of the 2009 IEEE Information Theory Workshop, 2009

Learning in the presence of noise.
Proceedings of the 1st International Conference on Game Theory for Networks, 2009

2008
Correlated Anarchy in Overlapping Wireless Networks.
IEEE J. Sel. Areas Commun., 2008

Vertical handover between wireless service providers.
Proceedings of the 6th International Symposium on Modeling and Optimization in Mobile, 2008

Vertical Handover between Wireless Standards.
Proceedings of IEEE International Conference on Communications, 2008

2007
The simplex game: can selfish users learn to operate efficiently in wireless networks?
Proceedings of the 2nd International Conference on Performance Evaluation Methodolgies and Tools, 2007


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