17 citations · 38 across the 18 of their papers we have counts for
6 papers · 1 filter
Near-Optimal No-Regret Learning for Correlated Equilibria in Multi-Player General-Sum Games
Ioannis Anagnostides, Constantinos Daskalakis, Gabriele Farina +3
Recently, Daskalakis, Fishelson, and Golowich (DFG) (NeurIPS`21) showed that if all agents in a multi-player general-sum normal-form game employ Optimistic Multiplicative Weights U…
Almost Universally Optimal Distributed Laplacian Solvers via Low-Congestion Shortcuts
Ioannis Anagnostides, Christoph Lenzen, Bernhard Haeupler +2
In this paper, we refine the (almost) \emph{existentially optimal} distributed Laplacian solver recently developed by Forster, Goranci, Liu, Peng, Sun, and Ye (FOCS `21) into an (a…
Frequency-Domain Representation of First-Order Methods: A Simple and Robust Framework of Analysis
Ioannis Anagnostides, Ioannis Panageas
Motivated by recent applications in min-max optimization, we employ tools from nonlinear control theory in order to analyze a class of "historical" gradient-based methods, for whic…
Dimensionality, Coordination, and Robustness in Voting
Ioannis Anagnostides, Dimitris Fotakis, Panagiotis Patsilinakos
We study the performance of voting mechanisms from a utilitarian standpoint, under the recently introduced framework of metric-distortion, offering new insights along three main li…
Deterministic Distributed Algorithms and Lower Bounds in the Hybrid Model
Ioannis Anagnostides, Themis Gouleakis
The $\hybrid$ model was recently introduced by Augustine et al. \cite{DBLP:conf/soda/AugustineHKSS20} in order to characterize from an algorithmic standpoint the capabilities of ne…
Metric-Distortion Bounds under Limited Information
Ioannis Anagnostides, Dimitris Fotakis, Panagiotis Patsilinakos
In this work we study the metric distortion problem in voting theory under a limited amount of ordinal information. Our primary contribution is threefold. First, we consider mechan…