89 citations · 430 across the 37 of their papers we have counts for
37 papers
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…
A Graph Sufficiency Perspective for Neural Networks
Cencheng Shen, Yuexiao Dong
This paper analyzes neural networks through graph variables and statistical sufficiency. We interpret neural network layers as graph-based transformations, where neurons act as pai…
Graph Neural Networks Powered by Encoder Embedding for Improved Node Learning
Shiyu Chen, Cencheng Shen, Youngser Park +1
Graph neural networks (GNNs) have emerged as a powerful framework for a wide range of node-level graph learning tasks. However, their performance typically depends on random or min…
Linear Discriminant Analysis with Gradient Optimization
Cencheng Shen, Yuexiao Dong
Linear discriminant analysis (LDA) is a fundamental classification and dimension reduction method that achieves Bayes optimality under Gaussian mixture, but often struggles in high…
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…
Principal Graph Encoder Embedding and Principal Community Detection
Cencheng Shen, Yuexiao Dong, Carey E. Priebe +3
In this paper, we introduce the concept of principal communities and propose a principal graph encoder embedding method that concurrently detects these communities and achieves ver…
Efficient Graph Encoder Embedding for Large Sparse Graphs in Python
Xihan Qin, Cencheng Shen
Graph is a ubiquitous representation of data in various research fields, and graph embedding is a prevalent machine learning technique for capturing key features and generating fix…
Fast and Scalable Multi-Kernel Encoder Classifier
Cencheng Shen
This paper introduces a new kernel-based classifier by viewing kernel matrices as generalized graphs and leveraging recent progress in graph embedding techniques. The proposed meth…
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…
Refined Graph Encoder Embedding via Self-Training and Latent Community Recovery
Cencheng Shen, Jonathan Larson, Ha Trinh +1
This paper introduces a refined graph encoder embedding method, enhancing the original graph encoder embedding through linear transformation, self-training, and hidden community re…
Edge-Parallel Graph Encoder Embedding
Ariel Lubonja, Cencheng Shen, Carey Priebe +1
New algorithms for embedding graphs have reduced the asymptotic complexity of finding low-dimensional representations. One-Hot Graph Encoder Embedding (GEE) uses a single, linear p…
Learning sources of variability from high-dimensional observational studies
Eric W. Bridgeford, Jaewon Chung, Brian Gilbert +6
Causal inference studies whether the presence of a variable influences an observed outcome. As measured by quantities such as the "average treatment effect," this paradigm is emplo…
Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamics
Cencheng Shen, Jonathan Larson, Ha Trinh +3
Analyzing large-scale time-series network data, such as social media and email communications, poses a significant challenge in understanding social dynamics, detecting anomalies,…
Synergistic Graph Fusion via Encoder Embedding
Cencheng Shen, Carey E. Priebe, Jonathan Larson +1
In this paper, we introduce a method called graph fusion embedding, designed for multi-graph embedding with shared vertex sets. Under the framework of supervised learning, our meth…
Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection
Cencheng Shen, Youngser Park, Carey E. Priebe
In this paper, we introduce a novel and computationally efficient method for vertex embedding, community detection, and community size determination. Our approach leverages a norma…
One-Hot Graph Encoder Embedding
Cencheng Shen, Qizhe Wang, Carey E. Priebe
In this paper we propose a lightning fast graph embedding method called one-hot graph encoder embedding. It has a linear computational complexity and the capacity to process billio…
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…
hyppo: A Multivariate Hypothesis Testing Python Package
Sambit Panda, Satish Palaniappan, Junhao Xiong +4
We introduce hyppo, a unified library for performing multivariate hypothesis testing, including independence, two-sample, and k-sample testing. While many multivariate independence…
Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities
Ronan Perry, Ronak Mehta, Richard Guo +6
Information-theoretic quantities, such as conditional entropy and mutual information, are critical data summaries for quantifying uncertainty. Current widely used approaches for co…
Sparse Representation Classification via Screening for Graphs
Cencheng Shen, Li Chen, Yuexiao Dong +1
The sparse representation classifier (SRC) is shown to work well for image recognition problems that satisfy a subspace assumption. In this paper we propose a new implementation of…