6 citations · 6 across the 2 of their papers we have counts for
5 papers
Computational Lower Bounds for Regret Minimization in Normal-Form Games
Ioannis Anagnostides, Alkis Kalavasis, Tuomas Sandholm
A celebrated connection in the interface of online learning and game theory establishes that players minimizing swap regret converge to correlated equilibria (CE) -- a seminal game…
Barriers to Welfare Maximization with No-Regret Learning
Ioannis Anagnostides, Alkis Kalavasis, Tuomas Sandholm
A celebrated result in the interface of online learning and game theory guarantees that the repeated interaction of no-regret players leads to a coarse correlated equilibrium (CCE)…
Convergence of for Gradient-Based Algorithms in Zero-Sum Games without the Condition Number: A Smoothed Analysis
Ioannis Anagnostides, Tuomas Sandholm
Gradient-based algorithms have shown great promise in solving large (two-player) zero-sum games. However, their success has been mostly confined to the low-precision regime since t…
Sampling and Optimal Preference Elicitation in Simple Mechanisms
Ioannis Anagnostides, Dimitris Fotakis, Panagiotis Patsilinakos
In this work we are concerned with the design of efficient mechanisms while eliciting limited information from the agents. First, we study the performance of sampling approximation…
Efficiently Computing Nash Equilibria in Adversarial Team Markov Games
Fivos Kalogiannis, Ioannis Anagnostides, Ioannis Panageas +3
Computing Nash equilibrium policies is a central problem in multi-agent reinforcement learning that has received extensive attention both in theory and in practice. However, provab…