Revenue Maximization for Market Intermediation with Correlated Priors. - Matthias Gerstgrasser; Paul W. Goldberg; Elias Koutsoupias (SAGT, 2016)

A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters. - Matthias Gerstgrasser; Sarah Nicholls; Michael Stout; Katherine Smart; Chris Powell; Theodore Kypraios; Dov J. Stekel (J. Bioinform. Comput. Biol., 2016)

Bayesian Gaussian Process Classification from Event-Related Brain Potentials in Alzheimer's Disease. - Wolfgang Fruehwirt; Pengfei Zhang 001; Matthias Gerstgrasser; Dieter Grossegger; Reinhold Schmidt; Thomas Benke; Peter Dal-Bianco; Gerhard Ransmayr; Leonard Weydemann; Heinrich Garn; Markus Waser; Michael A. Osborne; et al. (AIME, 2017)

On the Complexity of Optimal Correlated Auctions and Reverse Auctions. - Matthias Gerstgrasser (AAMAS, 2018)

Market intermediation: information, computation, and incentives. - Matthias Gerstgrasser (2018)

Multi-Unit Bilateral Trade. - Matthias Gerstgrasser; Paul W. Goldberg; Bart de Keijzer; Philip Lazos; Alexander Skopalik (AAAI, 2019)

Reverse auctions are different from auctions. - Matthias Gerstgrasser (Inf. Process. Lett., 2019)

Reinforcement Learning of Simple Indirect Mechanisms. - Gianluca Brero; Alon Eden; Matthias Gerstgrasser; David C. Parkes; Duncan Rheingans-Yoo (CoRR, 2020)

Reinforcement Learning of Sequential Price Mechanisms. - Gianluca Brero; Alon Eden; Matthias Gerstgrasser; David C. Parkes; Duncan Rheingans-Yoo (AAAI, 2021)

Promoting Resilience in Multi-Agent Reinforcement Learning via Confusion-Based Communication. - Ofir Abu; Matthias Gerstgrasser; Jeffrey S. Rosenschein; Sarah Keren (CoRR, 2021)

CrowdPlay: Crowdsourcing Human Demonstrations for Offline Learning. - Matthias Gerstgrasser; Rakshit Trivedi; David C. Parkes (ICLR, 2022)

Learning Stackelberg Equilibria and Applications to Economic Design Games. - Gianluca Brero; Alon Eden; Darshan Chakrabarti; Matthias Gerstgrasser; Vincent Li; David C. Parkes (CoRR, 2022)

Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning. - Matthias Gerstgrasser; David C. Parkes (ICML, 2023)

Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning. - Matthias Gerstgrasser; Tom Danino; Sarah Keren (NeurIPS, 2023)

Grounding or Guesswork? Large Language Models are Presumptive Grounders. - Omar Shaikh; Kristina Gligoric; Ashna Khetan; Matthias Gerstgrasser; Diyi Yang; Dan Jurafsky (CoRR, 2023)

Grounding Gaps in Language Model Generations. - Omar Shaikh; Kristina Gligoric; Ashna Khetan; Matthias Gerstgrasser; Diyi Yang; Dan Jurafsky (NAACL-HLT, 2024)

Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data. - Matthias Gerstgrasser; Rylan Schaeffer; Apratim Dey; Rafael Rafailov; Henry Sleight; John Hughes; Tomasz Korbak; Rajashree Agrawal; Dhruv Pai; Andrey Gromov; Daniel A. Roberts; Diyi Yang; et al. (CoRR, 2024)

Collapse or Thrive? Perils and Promises of Synthetic Data in a Self-Generating World. - Joshua Kazdan; Rylan Schaeffer; Apratim Dey; Matthias Gerstgrasser; Rafael Rafailov; David L. Donoho; Sanmi Koyejo (CoRR, 2024)

Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track. - Rylan Schaeffer; Joshua Kazdan; Yegor Denisov-Blanch; Brando Miranda; Matthias Gerstgrasser; Susan Zhang; Andreas Haupt; Isha Gupta; Elyas Obbad; Jesse Dodge; Jessica Zosa Forde; Koustuv Sinha; et al. (CoRR, 2025)