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20172021
most citedProving Expected Sensitivity of Probabilistic Programs

36 citations · 50 across the 6 of their papers we have counts for

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cs.LG2020

Scaling Guarantees for Nearest Counterfactual Explanations

Kiarash Mohammadi, Amir-Hossein Karimi, Gilles Barthe +1

Counterfactual explanations (CFE) are being widely used to explain algorithmic decisions, especially in consequential decision-making contexts (e.g., loan approval or pretrial bail…

cs.LG2020

A survey of algorithmic recourse: definitions, formulations, solutions, and prospects

Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf +1

Machine learning is increasingly used to inform decision-making in sensitive situations where decisions have consequential effects on individuals' lives. In these settings, in addi…

cs.LG2019

Privacy Amplification by Mixing and Diffusion Mechanisms

Borja Balle, Gilles Barthe, Marco Gaboardi +1

A fundamental result in differential privacy states that the privacy guarantees of a mechanism are preserved by any post-processing of its output. In this paper we investigate unde…

cs.LG2019

Model-Agnostic Counterfactual Explanations for Consequential Decisions

Amir-Hossein Karimi, Gilles Barthe, Borja Balle +1

Predictive models are being increasingly used to support consequential decision making at the individual level in contexts such as pretrial bail and loan approval. As a result, the…

cs.LG2018

Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences

Borja Balle, Gilles Barthe, Marco Gaboardi

Differential privacy comes equipped with multiple analytical tools for the design of private data analyses. One important tool is the so-called "privacy amplification by subsamplin…