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Impossibility of Characterizing Distribution Learning -- a simple solution to a long-standing problem
We show that there is no notion of dimension that characterizes the sample complexity of learning distribution classes.
Learning Losses for Strategic Classification
We analyse the sample complexity for a known graph of possible manipulations in terms of the complexity of the function class and the manipulation graph.
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.
