Search Results for author:
Found 16 papers, 4 papers with code
Network two-sample test for block models
We consider the two-sample testing problem for networks, where the goal is to determine whether two sets of networks originated from the same stochastic model.
kNN Algorithm for Conditional Mean and Variance Estimation with Automated Uncertainty Quantification and Variable Selection
In this paper, we introduce a kNN-based regression method that synergizes the scalability and adaptability of traditional non-parametric kNN models with a novel variable selection technique.
Temporal-spatial model via Trend Filtering
In such a context, we study the Trend Filtering, a nonparametric estimator introduced by \cite{mammen1997locally} and \cite{rudin1992nonlinear}.
Kernel Biclustering algorithm in Hilbert Spaces
Biclustering algorithms partition data and covariates simultaneously, providing new insights in several domains, such as analyzing gene expression to discover new biological functions.
Variance estimation in graphs with the fused lasso
We study the problem of variance estimation in general graph-structured problems.
Sequentially learning the topological ordering of causal directed acyclic graphs with likelihood ratio scores
We demonstrate a novel application of this general approach to estimate the topological ordering of a DAG.
Feature Grouping and Sparse Principal Component Analysis with Truncated Regularization
In this paper, we consider a new variant for principal component analysis (PCA), aiming to capture the grouping and/or sparse structures of factor loadings simultaneously.
Lattice partition recovery with dyadic CART
We study piece-wise constant signals corrupted by additive Gaussian noise over a $d$-dimensional lattice.
Optimal network online change point localisation
The goal is to detect the change point as quickly as possible, if it exists, subject to a constraint on the number or probability of false alarms.
Quantile regression with deep ReLU Networks: Estimators and minimax rates
Overall, the theoretical and empirical results provide insight into the strong performance of ReLU neural networks for quantile regression across a broad range of function classes and error distributions.
Extensions to the Proximal Distance Method of Constrained Optimization
The current paper studies the problem of minimizing a loss $f(\boldsymbol{x})$ subject to constraints of the form $\boldsymbol{D}\boldsymbol{x} \in S$, where $S$ is a closed set, convex or not, and $\boldsymbol{D}$ is a matrix that fuses parameters.
High Dimensional Latent Panel Quantile Regression with an Application to Asset Pricing
We propose a generalization of the linear panel quantile regression model to accommodate both \textit{sparse} and \textit{dense} parts: sparse means while the number of covariates available is large, potentially only a much smaller number of them have a nonzero impact on each conditional quantile of the response variable; while the dense part is represent by a low-rank matrix that can be approximated by latent factors and their loadings.
Learning Gaussian DAGs from Network Data
Structural learning of directed acyclic graphs (DAGs) or Bayesian networks has been studied extensively under the assumption that data are independent.
Vector-Space Markov Random Fields via Exponential Families
Specifically, VS-MRFs are the joint graphical model distributions where the node-conditional distributions belong to generic exponential families with general vector space domains.
Tensor decomposition with generalized lasso penalties
We present an approach for penalized tensor decomposition (PTD) that estimates smoothly varying latent factors in multi-way data.
Priors for Random Count Matrices Derived from a Family of Negative Binomial Processes
We define a family of probability distributions for random count matrices with a potentially unbounded number of rows and columns.
