(Arxiv) On the Role of Geometry in Solving Variational Inequalities
(with Ioannis Anagnostides and Ioannis Panageas) [Arxiv]
(Arxiv) The Complexity of Nash Equilibrium in Network Congestion and Coordination Games
(with Ioannis Anagnostides and Ioannis Panageas) [Arxiv]
(Arxiv) Finding a Positive Index Nash Equilibrium is PPADS-Complete
(with Andreas Kontogiannis, Ioannis Panageas, and Vasilis Pollatos) [Arxiv]
(SODA 2027) The Computational Complexity of Team Zero-Sum Games
(with Ioannis Anagnostides, Ioannis Panageas, and Tuomas Sandholm) [Arxiv]
(NeurIPS 2026) Efficient Computation and Best-Response Dynamics in Anonymous Two-Action Games with Linear Utilities
(with Michail Fasoulakis, Evangelos Markakis, Ioannis Panageas, and Christodoulos Santorinaios)
(ICML 2026) On the Computational Complexity of Performative Prediction
(with Ioannis Anagnostides, Rohan Chauhan, Ioannis Panageas, and Tuomas Sandholm) [Arxiv]
(NeurIPS 2025 Spotlight) The Complexity of Symmetric Equilibria in Min-Max Optimization and Team Zero-Sum Games
(with Ioannis Anagnostides, Ioannis Panageas, and Tuomas Sandholm) [Arxiv]
(NeurIPS 2025) The Complexity of Finding Local Optima in Contrastive Learning
(with Yiyuan Luo, Vaggos Chatziafratis, Ioannis Panageas, Parnian Shakhar, and Stelios Andrew Stavroulakis) [Arxiv]
(NeurIPS 2024) Learning Equilibria in Adversarial Team Markov Games: A Nonconvex-Hidden-Concave Min-Max Optimization Problem
(with Fivos Kalogiannis and Ioannis Panageas) [Arxiv]