Search Results for author: David M. Pennock

Found 5 papers, 2 papers with code

No-Regret and Incentive-Compatible Online Learning

1 code implementation ICML 2020 Rupert Freeman, David M. Pennock, Chara Podimata, Jennifer Wortman Vaughan

First, we want the learning algorithm to be no-regret with respect to the best fixed expert in hindsight.

Crowdsourced Outcome Determination in Prediction Markets

no code implementations14 Dec 2016 Rupert Freeman, Sebastien Lahaie, David M. Pennock

A prediction market is a useful means of aggregating information about a future event.

The Possibilities and Limitations of Private Prediction Markets

no code implementations24 Feb 2016 Rachel Cummings, David M. Pennock, Jennifer Wortman Vaughan

We consider the design of private prediction markets, financial markets designed to elicit predictions about uncertain events without revealing too much information about market participants' actions or beliefs.

Budget Constraints in Prediction Markets

no code implementations7 Oct 2015 Nikhil Devanur, Miroslav Dudík, Zhiyi Huang, David M. Pennock

We give a detailed characterization of optimal trades under budget constraints in a prediction market with a cost-function-based automated market maker.

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