114 citations · 813 across the 92 of their papers we have counts for
11 papers · 1 filter
Chaos persists in large-scale multi-agent learning despite adaptive learning rates
Emmanouil-Vasileios Vlatakis-Gkaragkounis, Lampros Flokas, Konstantinos Metaxas +1
Multi-agent learning is intrinsically harder, more unstable and unpredictable than single agent optimization. For this reason, numerous specialized heuristics and techniques have b…
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games
Mathieu Laurière, Sarah Perrin, Sertan Girgin +8
Mean Field Games (MFGs) have been introduced to efficiently approximate games with very large populations of strategic agents. Recently, the question of learning equilibria in MFGs…
Multi-agent Performative Prediction: From Global Stability and Optimality to Chaos
Georgios Piliouras, Fang-Yi Yu
The recent framework of performative prediction is aimed at capturing settings where predictions influence the target/outcome they want to predict. In this paper, we introduce a na…
Online Optimization in Games via Control Theory: Connecting Regret, Passivity and Poincaré Recurrence
Yun Kuen Cheung, Georgios Piliouras
We present a novel control-theoretic understanding of online optimization and learning in games, via the notion of passivity. Passivity is a fundamental concept in control theory,…
Efficient Online Learning for Dynamic k-Clustering
Dimitris Fotakis, Georgios Piliouras, Stratis Skoulakis
We study dynamic clustering problems from the perspective of online learning. We consider an online learning problem, called \textit{Dynamic -Clustering}, in which centers a…
Global Convergence of Multi-Agent Policy Gradient in Markov Potential Games
Stefanos Leonardos, Will Overman, Ioannis Panageas +1
Potential games are arguably one of the most important and widely studied classes of normal form games. They define the archetypal setting of multi-agent coordination as all agent…
Learning in Matrix Games can be Arbitrarily Complex
Gabriel P. Andrade, Rafael Frongillo, Georgios Piliouras
A growing number of machine learning architectures, such as Generative Adversarial Networks, rely on the design of games which implement a desired functionality via a Nash equilibr…
Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent
Dimitris Fotakis, Thanasis Lianeas, Georgios Piliouras +1
We consider a natural model of online preference aggregation, where sets of preferred items along with a demand for items in each , appear online…
Smooth markets: A basic mechanism for organizing gradient-based learners
David Balduzzi, Wojciech M Czarnecki, Thomas W Anthony +5
With the success of modern machine learning, it is becoming increasingly important to understand and control how learning algorithms interact. Unfortunately, negative results from…
Venn GAN: Discovering Commonalities and Particularities of Multiple Distributions
Yasin Yazıcı, Bruno Lecouat, Chuan-Sheng Foo +4
We propose a GAN design which models multiple distributions effectively and discovers their commonalities and particularities. Each data distribution is modeled with a mixture of $…
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…