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Found 10 papers, 4 papers with code
Bayesian Strategic Classification
We initiate the study of partial information release by the learner in strategic classification.
Sequential Strategic Screening
We initiate the study of strategic behavior in screening processes with multiple classifiers.
Multiaccurate Proxies for Downstream Fairness
The goal of the proxy is to allow a general "downstream" learner -- with minimal assumptions on their prediction task -- to be able to use the proxy to train a model that is fair with respect to the true sensitive features.
Adaptive Machine Unlearning
In this paper, we give a general reduction from deletion guarantees against adaptive sequences to deletion guarantees against non-adaptive sequences, using differential privacy and its connection to max information.
Lexicographically Fair Learning: Algorithms and Generalization
We extend the notion of minimax fairness in supervised learning problems to its natural conclusion: lexicographic minimax fairness (or lexifairness for short).
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
We study the data deletion problem for convex models.
Algorithms and Learning for Fair Portfolio Design
We consider a variation on the classical finance problem of optimal portfolio design.
A New Analysis of Differential Privacy's Generalization Guarantees
This second claim follows from a thought experiment in which we imagine that the dataset is resampled from the posterior distribution after the mechanism has committed to its answers.
Average Individual Fairness: Algorithms, Generalization and Experiments
Given a sample of individuals and classification problems, we design an oracle-efficient algorithm (i. e. one that is given access to any standard, fairness-free learning heuristic) for the fair empirical risk minimization task.
Differentially Private Fair Learning
This algorithm is appealingly simple, but must be able to use protected group membership explicitly at test time, which can be viewed as a form of 'disparate treatment'.
