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Jaewon Chung

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3 papers

1 author row

TMLR Journal 2024 Journal Article

Independence Testing for Temporal Data

Cencheng Shen
Jaewon Chung
Ronak Mehta
Ting Xu
Joshua T Vogelstein

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, such as relying on parametric assumptions, detecting only linear associations, and requiring multiple tests and corrections. While many non-parametric and universally consistent dependence measures have recently been proposed, directly applying them to temporal data can inflate the p-value and result in an invalid test. To address these challenges, this paper introduces the temporal dependence statistic with block permutation to test independence between temporal data. Under proper assumptions, the proposed procedure is asymptotically valid and universally consistent for testing independence between stationary time series, and capable of estimating the optimal dependence lag that maximizes the dependence. Moreover, it is compatible with a rich family of distance and kernel based dependence measures, eliminates the need for multiple testing, and exhibits excellent testing power in various simulation settings.

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JMLR Journal 2020 Journal Article

Sparse Projection Oblique Randomer Forests

Tyler M. Tomita
James Browne
Cencheng Shen
Jaewon Chung
Jesse L. Patsolic
Benjamin Falk
Carey E. Priebe
Jason Yim

Decision forests, including Random Forests and Gradient Boosting Trees, have recently demonstrated state-of-the-art performance in a variety of machine learning settings. Decision forests are typically ensembles of axis-aligned decision trees; that is, trees that split only along feature dimensions. In contrast, many recent extensions to decision forests are based on axis-oblique splits. Unfortunately, these extensions forfeit one or more of the favorable properties of decision forests based on axis-aligned splits, such as robustness to many noise dimensions, interpretability, or computational efficiency. We introduce yet another decision forest, called “Sparse Projection Oblique Randomer Forests” (SPORF). SPORF trees recursively split along very sparse random projections. Our method significantly improves accuracy over existing state-of-the-art algorithms on a standard benchmark suite for classification with $>100$ problems of varying dimension, sample size, and number of classes. To illustrate how SPORF addresses the limitations of both axis-aligned and existing oblique decision forest methods, we conduct extensive simulated experiments. SPORF typically yields improved performance over existing decision forest methods, while mitigating computational efficiency and scalability and maintaining interpretability. Very sparse random projections can be incorporated into gradient boosted trees to obtain potentially similar gains. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2020. ( edit, beta )

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JMLR Journal 2019 Journal Article

GraSPy: Graph Statistics in Python

Jaewon Chung
Benjamin D. Pedigo
Eric W. Bridgeford
Bijan K. Varjavand
Hayden S. Helm
Joshua T. Vogelstein

We introduce graspy, a Python library devoted to statistical inference, machine learning, and visualization of random graphs and graph populations. This package provides flexible and easy-to-use algorithms for analyzing and understanding graphs with a sklearn compliant API. graspy can be downloaded from Python Package Index (PyPi), and is released under the Apache 2.0 open-source license. The documentation and all releases are available at https://neurodata.io/graspy. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2019. ( edit, beta )

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