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02/13/2023
Characterizing notions of omniprediction via multicalibration
A recent line of work shows that notions of multigroup fairness imply su...
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10/16/2022
Loss Minimization through the Lens of Outcome Indistinguishability
We present a new perspective on loss minimization and the recent notion ...
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10/04/2022
Making Decisions under Outcome Performativity
Decision-makers often act in response to data-driven predictions, with t...
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06/23/2022
Backward baselines: Is your model predicting the past?
When does a machine learning model predict the future of individuals and...
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04/14/2022
Planting Undetectable Backdoors in Machine Learning Models
Given the computational cost and technical expertise required to train m...
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03/02/2022
Low-Degree Multicalibration
Introduced as a notion of algorithmic fairness, multicalibration has pro...
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07/12/2021
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration
When facing uncertainty, decision-makers want predictions they can trust...
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11/26/2020
Outcome Indistinguishability
Prediction algorithms assign numbers to individuals that are popularly u...
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02/27/2020
A Distributional Framework for Data Valuation
Shapley value is a classic notion from game theory, historically used to...
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04/22/2019
Tracking and Improving Information in the Service of Fairness
As algorithmic prediction systems have become widespread, fears that the...
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04/03/2019
Preference-Informed Fairness
As algorithms are increasingly used to make important decisions pertaini...
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05/31/2018
Multiaccuracy: Black-Box Post-Processing for Fairness in Classification
Machine learning predictors are successfully deployed in applications ra...
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04/26/2018
On Estimating Edit Distance: Alignment, Dimension Reduction, and Embeddings
Edit distance is a fundamental measure of distance between strings and h...
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03/08/2018
Fairness Through Computationally-Bounded Awareness
We study the problem of fair classification within the versatile framewo...
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11/22/2017