Efficient Failure Pattern Identification of Predictive Algorithms
This addresses the challenge of debugging predictive algorithms for practitioners, but it is incremental as it builds on existing exploration-exploitation trade-offs.
The paper tackles the problem of efficiently identifying misclassification patterns of a classifier on unlabeled data by proposing a human-machine collaborative framework with a sequential recommendation algorithm. The results show competitive performance on multiple datasets at various signal-to-noise ratios.
Given 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.