89 citations · 430 across the 37 of their papers we have counts for
18 papers · 1 filter
Decision Tree Embedding by Leaf-Means
Cencheng Shen, Yuexiao Dong, Carey E. Priebe
Decision trees and random forest remain highly competitive for classification on medium-sized, standard datasets due to their robustness, minimal preprocessing requirements, and in…
Explaining Categorical Feature Interactions Using Graph Covariance and LLMs
Cencheng Shen, Darren Edge, Jonathan Larson +1
Modern datasets often consist of numerous samples with abundant features and associated timestamps. Analyzing such datasets to uncover underlying events typically requires complex…
Encoder Embedding for General Graph and Node Classification
Cencheng Shen
Graph encoder embedding, a recent technique for graph data, offers speed and scalability in producing vertex-level representations from binary graphs. In this paper, we extend the…
A Simple Spectral Failure Mode for Graph Convolutional Networks
Carey E. Priebe, Cencheng Shen, Ningyuan Huang +1
Neural networks have achieved remarkable successes in machine learning tasks. This has recently been extended to graph learning using neural networks. However, there is limited the…
High-Dimensional Independence Testing via Maximum and Average Distance Correlations
Cencheng Shen, Yuexiao Dong
This paper investigates the utilization of maximum and average distance correlations for multivariate independence testing. We characterize their consistency properties in high-dim…
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…
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…
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…
Learning Interpretable Characteristic Kernels via Decision Forests
Sambit Panda, Cencheng Shen, Joshua T. Vogelstein
Decision forests are widely used for classification and regression tasks. A lesser known property of tree-based methods is that one can construct a proximity matrix from the tree(s…
The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing
Cencheng Shen, Joshua T. Vogelstein
Distance-based tests, also called "energy statistics", are leading methods for two-sample and independence tests from the statistics community. Kernel-based tests, developed from "…
From Distance Correlation to Multiscale Graph Correlation
Cencheng Shen, Carey E. Priebe, Joshua T. Vogelstein
Understanding and developing a correlation measure that can detect general dependencies is not only imperative to statistics and machine learning, but also crucial to general scien…
Discovering and Deciphering Relationships Across Disparate Data Modalities
Joshua T. Vogelstein, Eric Bridgeford, Qing Wang +3
Understanding the relationships between different properties of data, such as whether a connectome or genome has information about disease status, is becoming increasingly importan…
Sparse Projection Oblique Randomer Forests
Tyler M. Tomita, James Browne, Cencheng Shen +8
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…
Sparse Representation Classification Beyond L1 Minimization and the Subspace Assumption
Cencheng Shen, Li Chen, Yuexiao Dong +1
The sparse representation classifier (SRC) has been utilized in various classification problems, which makes use of L1 minimization and works well for image recognition satisfying…
Manifold Matching using Shortest-Path Distance and Joint Neighborhood Selection
Cencheng Shen, Joshua T. Vogelstein, Carey E. Priebe
Matching datasets of multiple modalities has become an important task in data analysis. Existing methods often rely on the embedding and transformation of each single modality with…
Robust Vertex Classification
Li Chen, Cencheng Shen, Joshua Vogelstein +1
For random graphs distributed according to stochastic blockmodels, a special case of latent position graphs, adjacency spectral embedding followed by appropriate vertex classificat…
Generalized Canonical Correlation Analysis for Classification
Cencheng Shen, Ming Sun, Minh Tang +1
For multiple multivariate data sets, we derive conditions under which Generalized Canonical Correlation Analysis (GCCA) improves classification performance of the projected dataset…
On the Incommensurability Phenomenon
Donniell E. Fishkind, Cencheng Shen, Youngser Park +1
Suppose that two large, multi-dimensional data sets are each noisy measurements of the same underlying random process, and principle components analysis is performed separately on…