Andreas Alexander Haupt

According to our database, Andreas Alexander Haupt authored at least 15 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning.

Ian Gemp, Andreas Alexander Haupt, Luke Marris, Siqi Liu, Georgios Piliouras

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

Formal contracts mitigate social dilemmas in multi-agent reinforcement learning.

Andreas A. Haupt, Phillip J. K. Christoffersen, Mehul Damani, Dylan Hadfield-Menell

Auton. Agents Multi Agent Syst., December, 2024

Risk preferences of learning algorithms.

Andreas A. Haupt, Aroon Narayanan

Games Econ. Behav., 2024

Convex Markov Games: A Framework for Fairness, Imitation, and Creativity in Multi-Agent Learning.

Ian Gemp, Andreas A. Haupt, Luke Marris, Siqi Liu, Georgios Piliouras

CoRR, 2024

Steering No-Regret Learners to a Desired Equilibrium.

Brian Hu Zhang, Gabriele Farina, Ioannis Anagnostides, Federico Cacciamani, Stephen McAleer, Andreas A. Haupt, Andrea Celli, Nicola Gatti, Vincent Conitzer, Tuomas Sandholm

Proceedings of the 25th ACM Conference on Economics and Computation, 2024

Certification Design for a Competitive Market.

Andreas Alexander Haupt, Nicole Immorlica, Brendan Lucier

Proceedings of the 25th ACM Conference on Economics and Computation, 2024

Black-Box Access is Insufficient for Rigorous AI Audits.

Stephen Casper, Carson Ezell, Charlotte Siegmann, Noam Kolt, Taylor Lynn Curtis, Benjamin Bucknall, Andreas A. Haupt, Kevin Wei, Jérémy Scheurer, Marius Hobbhahn, Lee Sharkey, Satyapriya Krishna, Marvin Von Hagen, Silas Alberti, Alan Chan, Qinyi Sun, Michael Gerovitch, David Bau, Max Tegmark, David Krueger, Dylan Hadfield-Menell

Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024

2023

Steering No-Regret Learners to Optimal Equilibria.

Brian Hu Zhang, Gabriele Farina, Ioannis Anagnostides, Federico Cacciamani, Stephen Marcus McAleer, Andreas Alexander Haupt, Andrea Celli, Nicola Gatti, Vincent Conitzer, Tuomas Sandholm

CoRR, 2023

Recommending to Strategic Users.

Andreas A. Haupt, Dylan Hadfield-Menell, Chara Podimata

CoRR, 2023

Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games.

Brian Hu Zhang, Gabriele Farina, Ioannis Anagnostides, Federico Cacciamani, Stephen McAleer, Andreas A. Haupt, Andrea Celli, Nicola Gatti, Vincent Conitzer, Tuomas Sandholm

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Get It in Writing: Formal Contracts Mitigate Social Dilemmas in Multi-Agent RL.

Phillip J. K. Christoffersen, Andreas A. Haupt, Dylan Hadfield-Menell

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

2022

Risk Aversion In Learning Algorithms and an Application To Recommendation Systems.

Andreas A. Haupt, Aroon Narayanan

CoRR, 2022

Contextually Private Mechanisms.

Andreas Alexander Haupt, Zoë Kettler Hitzig

Proceedings of the EC '22: The 23rd ACM Conference on Economics and Computation, Boulder, CO, USA, July 11, 2022

Towards Psychologically-Grounded Dynamic Preference Models.

Mihaela Curmei, Andreas A. Haupt, Benjamin Recht, Dylan Hadfield-Menell

Proceedings of the RecSys '22: Sixteenth ACM Conference on Recommender Systems, Seattle, WA, USA, September 18, 2022

2021

The Optimality of Upgrade Pricing.

Dirk Bergemann, Alessandro Bonatti, Andreas A. Haupt, Alex Smolin

Proceedings of the Web and Internet Economics - 17th International Conference, 2021