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Found 20 papers, 0 papers with code
A Graphical Point Process Framework for Understanding Removal Effects in Multi-Touch Attribution
Marketers employ various online advertising channels to reach customers, and they are particularly interested in attribution for measuring the degree to which individual touchpoints contribute to an eventual conversion.
Theoretical Guarantees for Sparse Principal Component Analysis based on the Elastic Net
Sparse principal component analysis (SPCA) has been widely used for dimensionality reduction and feature extraction in high-dimensional data analysis.
Nonlinear Sufficient Dimension Reduction for Distribution-on-Distribution Regression
We introduce a novel framework for nonlinear sufficient dimension reduction where both the predictor and the response are distributional data, which are modeled as members of a metric space.
An additive graphical model for discrete data
We introduce a nonparametric graphical model for discrete node variables based on additive conditional independence.
Dimension Reduction for Fréchet Regression
In this paper, we introduce a flexible sufficient dimension reduction (SDR) method for Fr\'echet regression to achieve two purposes: to mitigate the curse of dimensionality caused by high-dimensional predictors and to provide a visual inspection tool for Fr\'echet regression.
Robust High-Dimensional Regression with Coefficient Thresholding and its Application to Imaging Data Analysis
It is of importance to develop statistical techniques to analyze high-dimensional data in the presence of both complex dependence and possible outliers in real-world applications such as imaging data analyses.
Improving Neural Network Robustness through Neighborhood Preserving Layers
Finally, we empirically show that our designed network architecture is more robust against state-of-art gradient descent based attacks, such as a PGD attack on the benchmark datasets MNIST and CIFAR10.
Analyzing Attention Mechanisms through Lens of Sample Complexity and Loss Landscape
Attention mechanisms have advanced state-of-the-art deep learning models in many machine learning tasks.
A Manifold Proximal Linear Method for Sparse Spectral Clustering with Application to Single-Cell RNA Sequencing Data Analysis
This paper considers a widely adopted model for SSC, which can be formulated as an optimization problem over the Stiefel manifold with nonsmooth and nonconvex objective.
Fisher's combined probability test for high-dimensional covariance matrices
Testing large covariance matrices is of fundamental importance in statistical analysis with high-dimensional data.
Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold
However, most of the existing Riemannian stochastic algorithms require the objective function to be differentiable, and they do not apply to the case where the objective function is nonsmooth.
Understanding Attention Mechanisms
Attention mechanisms have advanced the state of the art in several machine learning tasks.
An Alternating Manifold Proximal Gradient Method for Sparse PCA and Sparse CCA
Sparse principal component analysis (PCA) and sparse canonical correlation analysis (CCA) are two essential techniques from high-dimensional statistics and machine learning for analyzing large-scale data.
Model-Based Clustering of Nonparametric Weighted Networks with Application to Water Pollution Analysis
The power of our proposed methods is demonstrated in simulation studies and a real application to sulfate pollution network analysis in Ohio watershed located in Pennsylvania, United States.
Model-Based Clustering of Time-Evolving Networks through Temporal Exponential-Family Random Graph Models
To choose the number of communities, we use conditional likelihood to construct an effective model selection criterion.
Inverse Moment Methods for Sufficient Forecasting using High-Dimensional Predictors
Assuming that the predictors affect the response through the latent factors, we propose to first conduct factor analysis and then apply sufficient dimension reduction on the estimated factors, to derive the reduced data for subsequent forecasting.
Nonparametric mixture of Gaussian graphical models
Graphical model has been widely used to investigate the complex dependence structure of high-dimensional data, and it is common to assume that observed data follow a homogeneous graphical model.
Joint limiting laws for high-dimensional independence tests
Using extreme-value form statistics to test against sparse alternatives and using quadratic form statistics to test against dense alternatives are two important testing procedures for high-dimensional independence.
Sufficient Forecasting Using Factor Models
Our method and theory allow the number of predictors to be larger than the number of observations.
Strong oracle optimality of folded concave penalized estimation
Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation.
