Efficient Failure Pattern Identification of Predictive Algorithms
Refereed conference paper presented and published in conference proceedings

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AbstractGiven a (machine learning) classifier and a collection of unlabeled data, how can we efficiently identify misclassification patterns presented in this dataset? To address this problem, we propose a human-machine collaborative framework that consists of a team of human annotators and a sequential recommendation algorithm. The recommendation algorithm is conceptualized as a stochastic sampler that, in each round, queries the annotators a subset of samples for their true labels and obtains the feedback information on whether the samples are misclassified. The sampling mechanism needs to balance between discovering new patterns of misclassification (exploration) and confirming the potential patterns of classification (exploitation). We construct a determinantal point process, whose intensity balances the exploration-exploitation trade-off through the weighted update of the posterior at each round to form the generator of the stochastic sampler. The numerical results empirically demonstrate the competitive performance of our framework on multiple datasets at various signal-to-noise ratios.
All Author(s) ListBao Nguyen, Viet Anh Nguyen
Name of ConferenceConference on Uncertainty in Artificial Intelligence
Start Date of Conference31/07/2023
End Date of Conference04/08/2023
Place of ConferencePittsburgh
Country/Region of ConferenceUnited States of America
LanguagesEnglish-United States

Last updated on 2023-14-09 at 12:33