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   Wu, Jimmy; Chen, Yatong; Liu, Yang (January 2022, International Conference on Machine Learning (ICML))

   We study the problem of classifier derandomization in machine learning: given a stochastic binary classifier f:X→[0,1], sample a deterministic classifier f̂ :X→{0,1} that approximates the output of f in aggregate over any data distribution. Recent work revealed how to efficiently derandomize a stochastic classifier with strong output approximation guarantees, but at the cost of individual fairness -- that is, if f treated similar inputs similarly, f̂ did not. In this paper, we initiate a systematic study of classifier derandomization with metric fairness guarantees. We show that the prior derandomization approach is almost maximally metric-unfair, and that a simple ``random threshold'' derandomization achieves optimal fairness preservation but with weaker output approximation. We then devise a derandomization procedure that provides an appealing tradeoff between these two: if f is α-metric fair according to a metric d with a locality-sensitive hash (LSH) family, then our derandomized f̂ is, with high probability, O(α)-metric fair and a close approximation of f. We also prove generic results applicable to all (fair and unfair) classifier derandomization procedures, including a bias-variance decomposition and reductions between various notions of metric fairness. 

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2. Metric-Fair Classifier Derandomization

   Jimmy Wu; Yatong Chen; Yang Liu (July 2022, International Conference on Machine Learning)
3. Quantum Machine Learning Classifier

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   Leider, Avery; Jaoude, Gio Abou; Strobel, Abigail E.; Mosley, Pauline (January 2022, Advances in Information and Communication: Proceedings of the 2022 Future of Information and Communication Conference (FICC), Volume 1)

   Arai, Kohei (Ed.)

   This Quantum Machine Learning Classifier (QMLC) uses the mathematics of quantum computing in a deep neural network to find and classify the specific flower type of the three different iris flower species: Versicolor, Setosa and Virginica, utilizing the SciKit-Learn dataset ``Iris.'' In that dataset, there are four characteristic features of each iris type: petal length, petal width, sepal length, and sepal width. The quantum computing machine learning classifier out-performed the classical deep learning neural network methods. Significant is that this classifier trained in fewer epochs. 

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5. Boxer: Interactive Comparison of Classifier Results

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   Gleicher, Michael; Barve, Aditya; Yu, Xinyi; Heimerl, Florian (June 2020, Computer Graphics Forum)

   null (Ed.)