3 citations · 3 across the 8 of their papers we have counts for
10 papers
The Complexity of Computing Nash Equilibria in Colonel Blotto Games
Vasilis Pollatos, Andreas Kontogiannis
We study the complexity of Nash equilibrium computation in discrete Colonel Blotto games. For two-player general-sum Colonel Blotto with monotonic piecewise-constant battlefield pa…
Finding a Positive Index Nash Equilibrium is PPADS-Complete
Andreas Kontogiannis, Ioannis Panageas, Vasilis Pollatos +1
Every nondegenerate bimatrix game has a Nash equilibrium of Shapley index +1, since all equilibria are isolated, have index +1 or -1, and their indices sum to +1. We prove that the…
The Complexity of Computing Coarse Correlated Equilibria in Markov Games with a Single Controller
Gabriele Farina, Andreas Kontogiannis, Ioannis Panageas +1
We study the complexity of computing stationary Markov coarse correlated equilibria (CCE) in discounted single-controller stochastic (Markov) games [PR81, FV97], a fundamental subc…
Online Learning on Hidden-Convex Losses via Algorithmic Equivalence: Optimal Regret, Geometric Barrier, and Bandit Feedback
Anas Barakat, Andreas Kontogiannis, Vasilis Pollatos +2
We study adversarial online learning with hidden-convex losses, i.e., nonconvex losses that become convex after a nonlinear reparameterization. Ghai, Lu and Hazan (2022) proved tha…
The Computational Complexity of Avoiding Strict Saddle Points in Constrained Optimization
Andreas Kontogiannis, Ioannis Panageas, Vasilis Pollatos
While first-order stationary points (FOSPs) are the traditional targets of non-convex optimization, they often correspond to undesirable strict saddle points. To circumvent this, a…
Efficient Swap Regret Minimization in Combinatorial Bandits
Andreas Kontogiannis, Vasilis Pollatos, Panayotis Mertikopoulos +1
This paper addresses the problem of designing efficient no-swap regret algorithms for combinatorial bandits, where the number of actions is exponentially large in the dimension…
Efficient Kernelized Learning in Polyhedral Games Beyond Full-Information: From Colonel Blotto to Congestion Games
Andreas Kontogiannis, Vasilis Pollatos, Gabriele Farina +2
We examine the problem of efficiently learning coarse correlated equilibria (CCE) in polyhedral games, that is, normal-form games with an exponentially large number of actions per…
On Corruption-Robustness in Performative Reinforcement Learning
Vasilis Pollatos, Debmalya Mandal, Goran Radanovic
In performative Reinforcement Learning (RL), an agent faces a policy-dependent environment: the reward and transition functions depend on the agent's policy. Prior work on performa…
Tree-based Focused Web Crawling with Reinforcement Learning
Andreas Kontogiannis, Dimitrios Kelesis, Vasilis Pollatos +2
A focused crawler aims at discovering as many web pages and web sites relevant to a target topic as possible, while avoiding irrelevant ones. Reinforcement Learning (RL) has been a…
Land Cover Semantic Segmentation Using ResUNet
Vasilis Pollatos, Loukas Kouvaras, Eleni Charou
In this paper we present our work on developing an automated system for land cover classification. This system takes a multiband satellite image of an area as input and outputs the…