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Matthias Gerstgrasser
Matthias Gerstgrasser
在 stanford.edu 的电子邮件经过验证 - 首页
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引用次数
引用次数
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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
332019
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
24*2021
Oracles & followers: Stackelberg equilibria in deep multi-agent reinforcement learning
M Gerstgrasser, DC Parkes
International Conference on Machine Learning, 11213-11236, 2023
132023
Grounding Gaps in Language Model Generations
O Shaikh, K Gligorić, A Khetan, M Gerstgrasser, D Yang, D Jurafsky
Proceedings of NAACL 2024, 2023
12*2023
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
92024
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
92021
Revenue maximization for market intermediation with correlated priors
M Gerstgrasser, PW Goldberg, E Koutsoupias
Algorithmic Game Theory: 9th International Symposium, SAGT 2016, Liverpool …, 2016
92016
Learning stackelberg equilibria and applications to economic design games
G Brero, D Chakrabarti, A Eden, M Gerstgrasser, V Li, DC Parkes
72022
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
72019
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
72017
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
62017
Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning
M Gerstgrasser, T Danino, S Keren
Advances in Neural Information Processing Systems 36, 2023
42023
CrowdPlay: Crowdsourcing Human Demonstrations for Offline Learning
M Gerstgrasser, R Trivedi, DC Parkes
International Conference on Learning Representations, 2022
42022
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
32022
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
32016
On the complexity of optimal correlated auctions and reverse auctions
M Gerstgrasser
Proceedings of the 17th International Conference on Autonomous Agents and …, 2018
22018
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
12022
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
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