16 citations · 41 across the 7 of their papers we have counts for
8 papers
BanditPAM++: Faster -medoids Clustering
Mo Tiwari, Ryan Kang, Donghyun Lee +4
Clustering is a fundamental task in data science with wide-ranging applications. In -medoids clustering, cluster centers must be actual datapoints and arbitrary distance metrics…
BanditPAM: Almost Linear Time -Medoids Clustering via Multi-Armed Bandits
Mo Tiwari, Martin Jinye Zhang, James Mayclin +3
Clustering is a ubiquitous task in data science. Compared to the commonly used -means clustering, -medoids clustering requires the cluster centers to be actual data points an…
Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits
Martin J. Zhang, James Zou, David Tse
Monte Carlo (MC) permutation test is considered the gold standard for statistical hypothesis testing, especially when standard parametric assumptions are not clear or likely to fai…
Minimax Optimality of Sign Test for Paired Heterogeneous Data
Martin J. Zhang, Meisam Razaviyayn, David Tse
Comparing two groups under different conditions is ubiquitous in the biomedical sciences. In many cases, samples from the two groups can be naturally paired; for example a pair of…
NeuralFDR: Learning Discovery Thresholds from Hypothesis Features
Fei Xia, Martin J. Zhang, James Zou +1
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…
Medoids in almost linear time via multi-armed bandits
Vivek Bagaria, Govinda M. Kamath, Vasilis Ntranos +2
Computing the medoid of a large number of points in high-dimensional space is an increasingly common operation in many data science problems. We present an algorithm Med-dit which…