5 citations · 7 across the 8 of their papers we have counts for
8 papers
Robust equilibria in continuous games: From strategic to dynamic robustness
Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos +1
In this paper, we examine the robustness of Nash equilibria in continuous games, under both strategic and dynamic uncertainty. Starting with the former, we introduce the notion of…
Multi-agent learning under uncertainty: Recurrence vs. concentration
Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos +1
In this paper, we examine the convergence landscape of multi-agent learning under uncertainty. Specifically, we analyze two stochastic models of regularized learning in continuous…
Accelerated regularized learning in finite N-person games
Kyriakos Lotidis, Angeliki Giannou, Panayotis Mertikopoulos +1
Motivated by the success of Nesterov's accelerated gradient algorithm for convex minimization problems, we examine whether it is possible to achieve similar performance gains in th…
Payoff-based learning with matrix multiplicative weights in quantum games
Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos +1
In this paper, we study the problem of learning in quantum games - and other classes of semidefinite games - with scalar, payoff-based feedback. For concreteness, we focus on the w…
Learning in quantum games
Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos
In this paper, we introduce a class of learning dynamics for general quantum games, that we call "follow the quantum regularized leader" (FTQL), in reference to the classical "foll…
On the convergence of policy gradient methods to Nash equilibria in general stochastic games
Angeliki Giannou, Kyriakos Lotidis, Panayotis Mertikopoulos +1
Learning in stochastic games is a notoriously difficult problem because, in addition to each other's strategic decisions, the players must also contend with the fact that the game…
Learning in Games with Quantized Payoff Observations
Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos
This paper investigates the impact of feedback quantization on multi-agent learning. In particular, we analyze the equilibrium convergence properties of the well-known "follow the…
A Bridge between Liquid and Social Welfare in Combinatorial Auctions with Submodular Bidders
Dimitris Fotakis, Kyriakos Lotidis, Chara Podimata
We study incentive compatible mechanisms for Combinatorial Auctions where the bidders have submodular (or XOS) valuations and are budget-constrained. Our objective is to maximize t…