| Associations of event-related brain potentials and Alzheimer’s disease severity: A longitudinal study W Fruehwirt, G Dorffner, S Roberts, M Gerstgrasser, D Grossegger, ... Progress in Neuro-Psychopharmacology and Biological Psychiatry 92, 31-38, 2019 | 31 | 2019 |
| Reinforcement Learning of Sequential Price Mechanisms G Brero, A Eden, M Gerstgrasser, D Parkes, D Rheingans-Yoo Proceedings of the AAAI Conference on Artificial Intelligence 35 (6), 5219-5227, 2021 | 21* | 2021 |
| Grounding or guesswork? large language models are presumptive grounders O Shaikh, K Gligorić, A Khetan, M Gerstgrasser, D Yang, D Jurafsky arXiv preprint arXiv:2311.09144, 2023 | 8 | 2023 |
| Learning Stackelberg equilibria in sequential price mechanisms G Brero, D Chakrabarti, A Eden, M Gerstgrasser, V Li, DC Parkes Proc. ICML Workshop for Reinforcement Learning Theory, 2021 | 8 | 2021 |
| Revenue maximization for market intermediation with correlated priors M Gerstgrasser, PW Goldberg, E Koutsoupias Algorithmic Game Theory: 9th International Symposium, SAGT 2016, Liverpool …, 2016 | 8 | 2016 |
| Multi-unit bilateral trade M Gerstgrasser, PW Goldberg, B de Keijzer, P Lazos, A Skopalik Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 1973-1980, 2019 | 7 | 2019 |
| Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer's disease W Fruehwirt, M Gerstgrasser, P Zhang, L Weydemann, M Waser, ... arXiv preprint arXiv:1711.08359, 2017 | 7 | 2017 |
| Oracles & followers: Stackelberg equilibria in deep multi-agent reinforcement learning M Gerstgrasser, DC Parkes International Conference on Machine Learning, 11213-11236, 2023 | 6 | 2023 |
| Bayesian Gaussian Process Classification from Event-Related Brain Potentials in Alzheimer’s Disease W Fruehwirt, P Zhang, M Gerstgrasser, D Grossegger, R Schmidt, ... Artificial Intelligence in Medicine: 16th Conference on Artificial …, 2017 | 5 | 2017 |
| A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters M Gerstgrasser, S Nicholls, M Stout, K Smart, C Powell, T Kypraios, ... Journal of Bioinformatics and Computational Biology 14 (03), 1650007, 2016 | 4 | 2016 |
| Learning stackelberg equilibria and applications to economic design games G Brero, D Chakrabarti, A Eden, M Gerstgrasser, V Li, DC Parkes | 2 | 2022 |
| CrowdPlay: Crowdsourcing Human Demonstrations for Offline Learning M Gerstgrasser, R Trivedi, DC Parkes International Conference on Learning Representations, 2022 | 2 | 2022 |
| On the complexity of optimal correlated auctions and reverse auctions M Gerstgrasser Proceedings of the 17th International Conference on Autonomous Agents and …, 2018 | 2 | 2018 |
| Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning M Gerstgrasser, T Danino, S Keren Advances in Neural Information Processing Systems 36, 2023 | 1 | 2023 |
| Meta-RL for Multi-Agent RL: Learning to Adapt to Evolving Agents M Gerstgrasser, DC Parkes Sixth Workshop on Meta-Learning at the Conference on Neural Information …, 2022 | 1 | 2022 |
| Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data M Gerstgrasser, R Schaeffer, A Dey, R Rafailov, H Sleight, J Hughes, ... arXiv preprint arXiv:2404.01413, 2024 | | 2024 |
| Stackelberg POMDP: A Reinforcement Learning Approach for Economic Design G Brero, A Eden, D Chakrabarti, M Gerstgrasser, V Li, DC Parkes arXiv preprint arXiv:2210.03852, 2022 | | 2022 |
| Collaboration Promotes Group Resilience in Multi-Agent AI S Keren, M Gerstgrasser, O Abu, J Rosenschein arXiv preprint arXiv:2111.06614, 2021 | | 2021 |
| Reverse auctions are different from auctions M Gerstgrasser Information Processing Letters 147, 49-54, 2019 | | 2019 |
| Market intermediation: information, computation, and incentives M Gerstgrasser University of Oxford, 2018 | | 2018 |
David C. ParkesGeorge F. Colony Professor of Computer Science, Harvard UniversityVerified email at eecs.harvard.edu
