Search Results for author: Annie Liang

Found 12 papers, 0 papers with code

The Value of Context: Human versus Black Box Evaluators

no code implementations17 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.

Medical Diagnosis

Challenges in Variable Importance Ranking Under Correlation

no code implementations5 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).

Feature Correlation Interpretable Machine Learning

Information and Learning in Economic Theory

no code implementations14 Dec 2022 Annie Liang

These lecture notes accompany a one-semester graduate course on information and learning in economic theory.

The Transfer Performance of Economic Models

no code implementations10 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.

Algorithm Design: A Fairness-Accuracy Frontier

no code implementations18 Dec 2021 Annie Liang, Jay Lu, Xiaosheng Mu

Whether it is optimal to ban an input generally depends on the designer's preferences.

Fairness

How Flexible is that Functional Form? Quantifying the Restrictiveness of Theories

no code implementations17 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.

Data and Incentives

no code implementations11 Jun 2020 Annie Liang, Erik Madsen

"Big data" gives markets access to previously unmeasured characteristics of individual agents.

Dynamically Aggregating Diverse Information

no code implementations15 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.

Attribute

Measuring the Completeness of Theories

no code implementations15 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."

BIG-bench Machine Learning

Overabundant Information and Learning Traps

no code implementations21 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.

The Theory is Predictive, but is it Complete? An Application to Human Perception of Randomness

no code implementations21 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.

BIG-bench Machine Learning Decision Making

Optimal and Myopic Information Acquisition

no code implementations18 Mar 2017 Annie Liang, Xiaosheng Mu, Vasilis Syrgkanis

We consider the problem of optimal dynamic information acquisition from many correlated information sources.

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