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Martin J Zhang

2 accepted papers

2020

BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits

NeurIPS 2020poster

Clustering is a ubiquitous task in data science. Compared to the commonly used k-means clustering, k-medoids clustering requires the cluster centers to be actual data points and supports arbitrary distance metrics, which permits greater interpretability and the clustering of structured objects. Curr…

2017

NeuralFDR: Learning Discovery Thresholds from Hypothesis Features

NeurIPS 2017poster

As datasets grow richer, an important challenge is to leverage the full features in the data to maximize the number of useful discoveries while controlling for false positives. We address this problem in the context of multiple hypotheses testing, where for each hypothesis, we observe a p-value alon…