no code implementations • 15 Apr 2024 • Sendhil Mullainathan, Ashesh Rambachan
Facing a similar problem -- how to extract theoretical insights from their intuitions -- researchers often turned to ``anomalies:'' constructed examples that highlight flaws in an existing theory and spur the development of new ones.
no code implementations • 10 Apr 2024 • Jon Kleinberg, Sendhil Mullainathan
A computational agent is trying to learn to generate from this language; we say that the agent generates from L in the limit if after some finite point in the enumeration of L, the agent is able to produce new elements that come exclusively from L and that have not yet been presented by the adversary.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Chao-Chun Hsu, Shantanu Karnwal, Sendhil Mullainathan, Ziad Obermeyer, Chenhao Tan
Machine learning models depend on the quality of input data.
no code implementations • 8 Sep 2020 • Justine Zhang, Sendhil Mullainathan, Cristian Danescu-Niculescu-Mizil
Understanding what leads to effective conversations can aid the design of better computer-mediated communication platforms.
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."
1 code implementation • 28 Mar 2019 • Maithra Raghu, Katy Blumer, Greg Corrado, Jon Kleinberg, Ziad Obermeyer, Sendhil Mullainathan
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 prediction in these domains.
no code implementations • 11 Feb 2019 • Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan, Cass R. Sunstein
But with appropriate requirements in place, the use of algorithms will make it possible to more easily examine and interrogate the entire decision process, thereby making it far easier to know whether discrimination has occurred.
no code implementations • 1 Dec 2018 • Andrew C. Miller, Ziad Obermeyer, Sendhil Mullainathan
In a predictive task, we show that EKG-based models can be more stable than EHR-based models across different patient populations.
no code implementations • 1 Dec 2018 • Andrew C. Miller, Ziad Obermeyer, David M. Blei, John P. Cunningham, Sendhil Mullainathan
An electrocardiogram (EKG) is a common, non-invasive test that measures the electrical activity of a patient's heart.
no code implementations • 12 Sep 2018 • Jon Kleinberg, Sendhil Mullainathan
Thus, simplicity transforms disadvantage into bias against the disadvantaged group.
no code implementations • 4 Jul 2018 • Maithra Raghu, Katy Blumer, Rory Sayres, Ziad Obermeyer, Robert Kleinberg, Sendhil Mullainathan, Jon Kleinberg
Our central methodological finding is that Direct Uncertainty Prediction (DUP), training a model to predict an uncertainty score directly from the raw patient features, works better than Uncertainty Via Classification, the two-step process of training a classifier and postprocessing the output distribution to give an uncertainty score.
no code implementations • 5 Jul 2017 • Jens Ludwig, Sendhil Mullainathan, Jann Spiess
In this paper we present tools for applied researchers that re-purpose off-the-shelf methods from the computer-science field of machine learning to create a "discovery engine" for data from randomized controlled trials (RCTs).
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 • 16 May 2017 • Jon Kleinberg, Sendhil Mullainathan, Johan Ugander
In this work we study comparison-based choice functions, a simple but surprisingly rich class of functions capable of exhibiting so-called choice-set effects.
no code implementations • 19 Sep 2016 • Jon Kleinberg, Sendhil Mullainathan, Manish Raghavan
Recent discussion in the public sphere about algorithmic classification has involved tension between competing notions of what it means for a probabilistic classification to be fair to different groups.
no code implementations • 15 Jun 2016 • Ashton Anderson, Jon Kleinberg, Sendhil Mullainathan
An increasing number of domains are providing us with detailed trace data on human decisions in settings where we can evaluate the quality of these decisions via an algorithm.