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Delegating Data Collection in Decentralized Machine Learning
To address the lack of a priori knowledge regarding the optimal performance, we give a convex program that can adaptively and efficiently compute the optimal contract.
Impossibility results for fair representations
The goal of such representations is that a model trained on data under the representation (e. g., a classifier) will be guaranteed to respect some fairness constraints.
Impossibility results for fair representation
With the growing awareness to fairness in machine learning and the realization of the central role that data representation has in data processing tasks, there is an obvious interest in notions of fair data representations.
On Learnability with Computable Learners
We initiate a study of learning with computable learners and computable output predictors.
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