no code implementations • 17 Feb 2024 • Andrei Iakovlev, Annie Liang
Evaluations once solely within the domain of human experts (e. g., medical diagnosis by doctors) can now also be carried out by machine learning algorithms.
no code implementations • 5 Feb 2024 • Annie Liang, Thomas Jemielita, Andy Liaw, Vladimir Svetnik, Lingkang Huang, Richard Baumgartner, Jason M. Klusowski
Recently, several adjustments to marginal permutation utilizing feature knockoffs were proposed to address this issue, such as the variable importance measure known as conditional predictive impact (CPI).
no code implementations • 14 Dec 2022 • Annie Liang
These lecture notes accompany a one-semester graduate course on information and learning in economic theory.
no code implementations • 10 Feb 2022 • Isaiah Andrews, Drew Fudenberg, Lihua Lei, Annie Liang, Chaofeng Wu
Economists often estimate models using data from a particular domain, e. g. estimating risk preferences in a particular subject pool or for a specific class of lotteries.
no code implementations • 18 Dec 2021 • Annie Liang, Jay Lu, Xiaosheng Mu
Whether it is optimal to ban an input generally depends on the designer's preferences.
no code implementations • 17 Jul 2020 • Drew Fudenberg, Wayne Gao, Annie Liang
We propose a restrictiveness measure for economic models based on how well they fit synthetic data from a pre-defined class.
no code implementations • 11 Jun 2020 • Annie Liang, Erik Madsen
"Big data" gives markets access to previously unmeasured characteristics of individual agents.
no code implementations • 15 Oct 2019 • Annie Liang, Xiaosheng Mu, Vasilis Syrgkanis
An agent has access to multiple information sources, each of which provides information about a different attribute of an unknown state.
no code implementations • 15 Oct 2019 • Drew Fudenberg, Jon Kleinberg, Annie Liang, Sendhil Mullainathan
We use machine learning to provide a tractable measure of the amount of predictable variation in the data that a theory captures, which we call its "completeness."
no code implementations • 21 May 2018 • Annie Liang, Xiaosheng Mu
We develop a model of social learning from overabundant information: Short-lived agents sequentially choose from a large set of (flexibly correlated) information sources for prediction of an unknown state.
no code implementations • 21 Jun 2017 • Jon Kleinberg, Annie Liang, Sendhil Mullainathan
Overall, our results indicate that (i) there is a significant amount of structure in this problem that existing models have yet to capture and (ii) there are rich domains in which machine learning may provide a viable approach to testing completeness.
no code implementations • 18 Mar 2017 • Annie Liang, Xiaosheng Mu, Vasilis Syrgkanis
We consider the problem of optimal dynamic information acquisition from many correlated information sources.