Stochastic Gradient Descent Works Really Well for Stress Minimization. - Katharina Börsig; Ulrik Brandes; Barna Pásztor (GD, 2020)

On the impact of publicly available news and information transfer to financial markets. - Metod Jazbec; Barna Pásztor; Felix Faltings; Nino Antulov-Fantulin; Petter N. Kolm (CoRR, 2020)

Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning. - Barna Pásztor; Andreas Krause 001; Ilija Bogunovic (Trans. Mach. Learn. Res., 2023)

Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning. - Matej Jusup; Barna Pásztor; Tadeusz Janik; Kenan Zhang; Francesco Corman; Andreas Krause 001; Ilija Bogunovic (AAMAS, 2024)

Bandits with Preference Feedback: A Stackelberg Game Perspective. - Barna Pásztor; Parnian Kassraie; Andreas Krause 001 (NeurIPS, 2024)

Contextual Bilevel Reinforcement Learning for Incentive Alignment. - Vinzenz Thoma; Barna Pásztor; Andreas Krause 001; Giorgia Ramponi; Yifan Hu (NeurIPS, 2024)

Melting Pot Contest: Charting the Future of Generalized Cooperative Intelligence. - Rakshit Trivedi; Akbir Khan; Jesse Clifton; Lewis Hammond; Edgar A. Duéñez-Guzmán; Dipam Chakraborty; John P. Agapiou; Jayd Matyas; Alexander Sasha Vezhnevets; Barna Pásztor; Yunke Ao; Omar G. Younis; et al. (NeurIPS, 2024)

Stochastic Bilevel Optimization with Lower-Level Contextual Markov Decision Processes. - Vinzenz Thoma; Barna Pásztor; Andreas Krause 001; Giorgia Ramponi; Yifan Hu (CoRR, 2024)

Learning Collusion in Episodic, Inventory-Constrained Markets. - Paul Friedrich 001; Barna Pásztor; Giorgia Ramponi (AAMAS, 2025)

Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantees via Constrained Mean-Field Reinforcement Learning. - Matej Jusup; Kenan Zhang; Zhiyuan Hu; Barna Pásztor; Andreas Krause 001; Francesco Corman (CoRR, 2025)