A Deep Learning Pipeline for the Synthesis of Graphic Novels. - Thomas Melistas; Giannis Siglidis; Fivos Kalogiannis; Ilan Manouach (ICCC, 2021)

Teamwork makes von Neumann work: Min-Max Optimization in Two-Team Zero-Sum Games. - Fivos Kalogiannis; Ioannis Panageas; Emmanouil V. Vlatakis-Gkaragkounis (CoRR, 2021)

Efficiently Computing Nash Equilibria in Adversarial Team Markov Games. - Fivos Kalogiannis; Ioannis Anagnostides; Ioannis Panageas; Emmanouil V. Vlatakis-Gkaragkounis; Vaggos Chatziafratis; Stelios Andrew Stavroulakis (ICLR, 2023)

Towards convergence to Nash equilibria in two-team zero-sum games. - Fivos Kalogiannis; Ioannis Panageas; Emmanouil V. Vlatakis-Gkaragkounis (ICLR, 2023)

Zero-sum Polymatrix Markov Games: Equilibrium Collapse and Efficient Computation of Nash Equilibria. - Fivos Kalogiannis; Ioannis Panageas (NeurIPS, 2023)

Algorithms and Complexity for Computing Nash Equilibria in Adversarial Team Games. - Ioannis Anagnostides; Fivos Kalogiannis; Ioannis Panageas; Emmanouil-Vasileios Vlatakis-Gkaragkounis; Stephen McAleer (EC, 2023)

Computing Nash Equilibria in Potential Games with Private Uncoupled Constraints. - Nikolas Patris; Stelios Stavroulakis; Fivos Kalogiannis; Rose Zhang; Ioannis Panageas (AAAI, 2024)

Learning Equilibria in Adversarial Team Markov Games: A Nonconvex-Hidden-Concave Min-Max Optimization Problem. - Fivos Kalogiannis; Jingming Yan; Ioannis Panageas (NeurIPS, 2024)

Solving Zero-Sum Convex Markov Games. - Fivos Kalogiannis; Emmanouil-Vasileios Vlatakis-Gkaragkounis; Ian Gemp; Georgios Piliouras (CoRR, 2025)