432 citations · 599 across the 16 of their papers we have counts for
16 papers
From Predictive Algorithms to Automatic Generation of Anomalies
Sendhil Mullainathan, Ashesh Rambachan
How can we extract theoretical insights from machine learning algorithms? We take a familiar lesson: researchers often turn their intuitions into theoretical insights by constructi…
The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization
Jon Kleinberg, Sendhil Mullainathan, Manish Raghavan
Online platforms have a wealth of data, run countless experiments and use industrial-scale algorithms to optimize user experience. Despite this, many users seem to regret the time…
Characterizing the Value of Information in Medical Notes
Chao-Chun Hsu, Shantanu Karnwal, Sendhil Mullainathan +2
Machine learning models depend on the quality of input data. As electronic health records are widely adopted, the amount of data in health care is growing, along with complaints ab…
The Data Station: Combining Data, Compute, and Market Forces
Raul Castro Fernandez, Kyle Chard, Ben Blaiszik +7
This paper introduces Data Stations, a new data architecture that we are designing to tackle some of the most challenging data problems that we face today: access to sensitive data…
Measuring the Completeness of Theories
Drew Fudenberg, Jon Kleinberg, Annie Liang +1
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." We apply this me…
The Algorithmic Automation Problem: Prediction, Triage, and Human Effort
Maithra Raghu, Katy Blumer, Greg Corrado +3
In a wide array of areas, algorithms are matching and surpassing the performance of human experts, leading to consideration of the roles of human judgment and algorithmic predictio…