13 citations · 43 across the 18 of their papers we have counts for
23 papers
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…
Equilibrium Bandits: Learning Optimal Equilibria of Unknown Dynamics
Siddharth Chandak, Ilai Bistritz, Nicholas Bambos
Consider a decision-maker that can pick one out of actions to control an unknown system, for turns. The actions are interpreted as different configurations or policies. Hol…
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…
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…
Distributed stochastic optimization with large delays
Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos +2
One of the most widely used methods for solving large-scale stochastic optimization problems is distributed asynchronous stochastic gradient descent (DASGD), a family of algorithms…
No Weighted-Regret Learning in Adversarial Bandits with Delays
Ilai Bistritz, Zhengyuan Zhou, Xi Chen +2
Consider a scenario where a player chooses an action in each round out of rounds and observes the incurred cost after a delay of rounds. The cost functions and the…