504 citations · 2k across the 109 of their papers we have counts for
15 papers · 1 filter
The Chi-Square Test of Distance Correlation
Cencheng Shen, Sambit Panda, Joshua T. Vogelstein
Distance correlation has gained much recent attention in the data science community: the sample statistic is straightforward to compute and asymptotically equals zero if and only i…
Valid Two-Sample Graph Testing via Optimal Transport Procrustes and Multiscale Graph Correlation with Applications in Connectomics
Jaewon Chung, Bijan Varjavand, Jesus Arroyo +5
Testing whether two graphs come from the same distribution is of interest in many real world scenarios, including brain network analysis. Under the random dot product graph model,…
Universally Consistent K-Sample Tests via Dependence Measures
Sambit Panda, Cencheng Shen, Ronan Perry +4
The K-sample testing problem involves determining whether K groups of data points are each drawn from the same distribution. Analysis of variance is arguably the most classical met…
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks
Adam Li, Ronan Perry, Chester Huynh +6
Decision forests (Forests), in particular random forests and gradient boosting trees, have demonstrated state-of-the-art accuracy compared to other methods in many supervised learn…
AutoGMM: Automatic Gaussian Mixture Modeling in Python
Tingshan Liu, Thomas L. Athey, Benjamin D. Pedigo +1
The exponential growth of complex data demands fully automatic clustering. Gaussian mixture models (GMMs) provide uncertainty-aware grouping but often require expertise to specify…
Independence Testing for Temporal Data
Cencheng Shen, Jaewon Chung, Ronak Mehta +2
Temporal data are increasingly prevalent in modern data science. A fundamental question is whether two time series are related or not. Existing approaches often have limitations, s…