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Panayotis Mertikopoulos

49 papers · 2017–2025 · 7 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (8) 🌍 Conference Polyglot (7) πŸ—ΊοΈ Taxonomy Completionist (29) 🐝 Cross-Pollinator (15) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🏠 Conference Loyalist (23) 🀝 Dynamic Duo (10) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (20) πŸ† Keyword Champion (3) ⚑ Prolific Year (8) πŸ—ƒοΈ Keyword Collector (143) πŸ“ˆ Trend Setter ❓ The Questioner (2) πŸ’Ž Century Club (49) πŸ”₯ Unstoppable (9)

Conferences

NIPS (23) ICML (16) COLT (4) ICLR (3) AISTATS (1) ALT (1) JMLR (1)

Top co-authors

Kimon Antonakopoulos (10) Volkan Cevher (7) Franck Iutzeler (6) Nicholas Bambos (6) JΓ©rΓ΄me Malick (6) Zhengyuan Zhou (6) Yu-Guan Hsieh (5) Georgios Piliouras (5) AmΓ©lie Heliou (4) Angeliki Giannou (4)

Keywords

nash equilibrium (13) game theory (11) no-regret learning (9) non-convex optimization (8) online learning (8) stochastic optimization (7) convergence rate (7) regret bound (5) convergence analysis (4) multi-agent learning (4) multi-agent system (4) monotone operator (3) stochastic gradient (3) dual averaging (3) saddle point (3) variational inequality (3) bregman divergence (3) stochastic gradient descent (3) regularized learning (3) regret minimization (3)

Papers

The impact of uncertainty on regularized learning in games ICML 2025
Tamed Langevin sampling under weaker conditions AISTATS 2025
The Global Convergence Time of Stochastic Gradient Descent in Non-Convex Landscapes: Sharp Estimates via Large Deviations ICML 2025
What is the Long-Run Distribution of Stochastic Gradient Descent? A Large Deviations Analysis ICML 2024
Accelerated Regularized Learning in Finite N-Person Games NIPS 2024
No-regret Learning in Harmonic Games: Extrapolation in the Face of Conflicting Interests NIPS 2024
A Geometric Decomposition of Finite Games: Convergence vs. Recurrence under Exponential Weights ICML 2024
The Computational Complexity of Finding Second-Order Stationary Points ICML 2024
Payoff-based Learning with Matrix Multiplicative Weights in Quantum Games NIPS 2023
The Equivalence of Dynamic and Strategic Stability under Regularized Learning in Games NIPS 2023
Exploiting hidden structures in non-convex games for convergence to Nash equilibrium NIPS 2023
Riemannian stochastic optimization methods avoid strict saddle points NIPS 2023
Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism JMLR 2022
No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation NIPS 2022
On the convergence of policy gradient methods to Nash equilibria in general stochastic games NIPS 2022
Asymptotic Degradation of Linear Regression Estimates with Strategic Data Sources ALT 2022
The Dynamics of Riemannian Robbins-Monro Algorithms COLT 2022
AdaGrad Avoids Saddle Points ICML 2022
UnderGrad: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees ICML 2022
Nested Bandits ICML 2022
Adaptive Extra-Gradient Methods for Min-Max Optimization and Games ICLR 2021
The Limits of Min-Max Optimization Algorithms: Convergence to Spurious Non-Critical Sets ICML 2021
Zeroth-Order Non-Convex Learning via Hierarchical Dual Averaging ICML 2021
Fast Routing under Uncertainty: Adaptive Learning in Congestion Games via Exponential Weights NIPS 2021
The Last-Iterate Convergence Rate of Optimistic Mirror Descent in Stochastic Variational Inequalities COLT 2021
Survival of the strictest: Stable and unstable equilibria under regularized learning with partial information COLT 2021
Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach ICML 2021
Adaptive Learning in Continuous Games: Optimal Regret Bounds and Convergence to Nash Equilibrium COLT 2021
On the Rate of Convergence of Regularized Learning in Games: From Bandits and Uncertainty to Optimism and Beyond NIPS 2021
Adaptive First-Order Methods Revisited: Convex Minimization without Lipschitz Requirements NIPS 2021
Sifting through the noise: Universal first-order methods for stochastic variational inequalities NIPS 2021
Online Non-Convex Optimization with Imperfect Feedback NIPS 2020
No-Regret Learning and Mixed Nash Equilibria: They Do Not Mix NIPS 2020
On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems NIPS 2020
Online and stochastic optimization beyond Lipschitz continuity: A Riemannian approach ICLR 2020
A new regret analysis for Adam-type algorithms ICML 2020
Gradient-free Online Learning in Continuous Games with Delayed Rewards ICML 2020
Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games ICML 2020
Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling NIPS 2020
Cautious Regret Minimization: Online Optimization with Long-Term Budget Constraints ICML 2019
On the convergence of single-call stochastic extra-gradient methods NIPS 2019
An adaptive Mirror-Prox method for variational inequalities with singular operators NIPS 2019
Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile ICLR 2019
Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go? ICML 2018
Learning in Games with Lossy Feedback NIPS 2018
Bandit Learning in Concave N-Person Games NIPS 2018
Stochastic Mirror Descent in Variationally Coherent Optimization Problems NIPS 2017
Learning with Bandit Feedback in Potential Games NIPS 2017
Countering Feedback Delays in Multi-Agent Learning NIPS 2017