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Fast Feature Selection with Fairness Constraints
Francesco Quinzan · Rajiv Khanna · Moshik Hershcovitch · Sarel Cohen · Daniel Waddington · Tobias Friedrich · Michael Mahoney

Fri Jul 28 02:30 PM -- 03:00 PM (PDT) @
in 2nd Workshop on Formal Verification of Machine Learning »

We study the fundamental problem of selecting optimal features for model construction. This problem is computationally challenging on large datasets, even with the use of greedy algorithm variants. To address this challenge, we extend the adaptive query model, recently proposed for the greedy forward selection for submodular functions, to the faster paradigm of Orthogonal Matching Pursuit for non-submodular functions. The proposed algorithm achieves exponentially fast parallel run time in the adaptive query model, scaling much better than prior work. Furthermore, our extension allows the use of downward-closed constraints, which can be used to encode certain fairness criteria into the feature selection process. We prove strong approximation guarantees for the algorithm based on standard assumptions. These guarantees are applicable to many parametric models, including Generalized Linear Models. Finally, we demonstrate empirically that the proposed algorithm competes favorably with state-of-the-art techniques for feature selection, on real-world and synthetic datasets.

Author Information

Francesco Quinzan (University of Oxford)
Rajiv Khanna (Purdue University)
Moshik Hershcovitch (IBM Research)
Sarel Cohen (Hasso Plattner Institute)
Daniel Waddington (IBM Research)
Tobias Friedrich (Hasso Plattner Institute)
Michael Mahoney (UC Berkeley)

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