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How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal.

, , , and . NIPS, page 2346-2354. (2013)

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Addendum to Ä Market Framework for Eliciting Private Data"., , and . CoRR, (2017)A Characterization of Scoring Rules for Linear Properties., and . COLT, volume 23 of JMLR Proceedings, page 27.1-27.13. JMLR.org, (2012)Open Problem: Property Elicitation and Elicitation Complexity., , and . COLT, volume 49 of JMLR Workshop and Conference Proceedings, page 1655-1658. JMLR.org, (2016)Multi-Observation Elicitation., , , and . COLT, volume 65 of Proceedings of Machine Learning Research, page 449-464. PMLR, (2017)Vector-Valued Property Elicitation., and . COLT, volume 40 of JMLR Workshop and Conference Proceedings, page 710-727. JMLR.org, (2015)An Axiomatic Characterization of CFMMs and Equivalence to Prediction Markets., , and . CoRR, (2023)Computational Complexity of k-Block Conjugacy., and . CoRR, (2019)An Axiomatic Study of Scoring Rule Markets., and . ITCS, volume 94 of LIPIcs, page 15:1-15:20. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2018)Bounded-Loss Private Prediction Markets., and . NeurIPS, page 10456-10465. (2018)Unifying lower bounds on prediction dimension of convex surrogates., , and . NeurIPS, page 22046-22057. (2021)