English
Related papers

Related papers: Learning Local Dependence In Ordered Data

200 papers

Graph Laplacian learning, also known as network topology inference, is a problem of great interest to multiple communities. In Gaussian graphical models (GM), graph learning amounts to endowing covariance selection with the Laplacian…

Machine Learning · Computer Science 2024-02-14 Changhao Shi , Gal Mishne

We propose a new method for local distance metric learning based on sample similarity as side information. These local metrics, which utilize conical combinations of metric weight matrices, are learned from the pooled spatial…

Machine Learning · Computer Science 2019-02-25 YInjie Huang , Cong Li , Michael Georgiopoulos , Georgios C. Anagnostopoulos

We study the problem of learning local metrics for nearest neighbor classification. Most previous works on local metric learning learn a number of local unrelated metrics. While this "independence" approach delivers an increased flexibility…

Machine Learning · Computer Science 2012-09-17 Jun Wang , Adam Woznica , Alexandros Kalousis

We consider regression scenarios where it is natural to impose an order constraint on the coefficients. We propose an order-constrained version of L1-regularized regression for this problem, and show how to solve it efficiently using the…

Applications · Statistics 2017-06-01 Xiaotong Suo , Robert Tibshirani

Undirected graphs are often used to describe high dimensional distributions. Under sparsity conditions, the graph can be estimated using $\ell_1$-penalization methods. We propose and study the following method. We combine a multiple…

Machine Learning · Statistics 2012-01-11 Shuheng Zhou , Philipp Rutimann , Min Xu , Peter Buhlmann

Sparse linear inverse problems appear in a variety of settings, but often the noise contaminating observations cannot accurately be described as bounded by or arising from a Gaussian distribution. Poisson observations in particular are a…

Statistics Theory · Mathematics 2018-02-14 Xin Jiang , Patricia Reynaud-Bouret , Vincent Rivoirard , Laure Sansonnet , Rebecca Willett

We propose a unified data-driven framework based on inverse optimal transport that can learn adaptive, nonlinear interaction cost function from noisy and incomplete empirical matching matrix and predict new matching in various matching…

Machine Learning · Statistics 2018-11-01 Ruilin Li , Xiaojing Ye , Haomin Zhou , Hongyuan Zha

Classical penalized likelihood regression problems deal with the case that the independent variables data are known exactly. In practice, however, it is common to observe data with incomplete covariate information. We are concerned with a…

Methodology · Statistics 2010-08-04 Xiwen Ma , Bin Dai , Ronald Klein , Barbara E. K. Klein , Kristine E. Lee , Grace Wahba

Variational inference methods for latent variable statistical models have gained popularity because they are relatively fast, can handle large data sets, and have deterministic convergence guarantees. However, in practice it is unclear…

Methodology · Statistics 2017-03-22 Hachem Saddiki , Andrew C. Trapp , Patrick Flaherty

The standard margin-based structured prediction commonly uses a maximum loss over all possible structured outputs. The large-margin formulation including latent variables not only results in a non-convex formulation but also increases the…

Machine Learning · Computer Science 2019-06-25 Kevin Bello , Jean Honorio

Estimating the dependences between random variables, and ranking them accordingly, is a prevalent problem in machine learning. Pursuing frequentist and information-theoretic approaches, we first show that the p-value and the mutual…

Machine Learning · Computer Science 2012-07-02 Harald Steck

Given $n$ i.i.d. observations of a random vector $(X,Z)$, where $X$ is a high-dimensional vector and $Z$ is a low-dimensional index variable, we study the problem of estimating the conditional inverse covariance matrix $\Omega(z) =…

Machine Learning · Statistics 2014-12-25 Jialei Wang , Mladen Kolar

We study the nonparametric covariance estimation of a stationary Gaussian field X observed on a lattice. To tackle this issue, a neighborhood selection procedure has been recently introduced. This procedure amounts to selecting a…

Statistics Theory · Mathematics 2009-09-02 Nicolas Verzelen

Penalized logistic regression is extremely useful for binary classification with large number of covariates (higher than the sample size), having several real life applications, including genomic disease classification. However, the…

Methodology · Statistics 2023-04-10 Ayanendranath Basu , Abhik Ghosh , María Jaenada , Leandro Pardo

Inverse problems, i.e., estimating parameters of physical models from experimental data, are ubiquitous in science and engineering. The Bayesian formulation is the gold standard because it alleviates ill-posedness issues and quantifies…

Machine Learning · Statistics 2024-05-28 Sharmila Karumuri , Ilias Bilionis

Many popular specifications for Vector Autoregressions (VARs) with multivariate stochastic volatility are not invariant to the way the variables are ordered due to the use of a Cholesky decomposition for the error covariance matrix. We show…

Econometrics · Economics 2021-11-16 Joshua C. C. Chan , Gary Koop , Xuewen Yu

Linear regression and classification methods with repeated functional data are considered. For each statistical unit in the sample, a real-valued parameter is observed over time under different conditions related by some neighborhood…

Methodology · Statistics 2024-09-23 Issam-Ali Moindjié , Cristian Preda , Sophie Dabo-Niang

We consider the setting where a collection of time series, modeled as random processes, evolve in a causal manner, and one is interested in learning the graph governing the relationships of these processes. A special case of wide interest…

Machine Learning · Computer Science 2016-08-30 Hossein Hosseini , Sreeram Kannan , Baosen Zhang , Radha Poovendran

Sparse structure learning in high-dimensional Gaussian graphical models is an important problem in multivariate statistical signal processing; since the sparsity pattern naturally encodes the conditional independence relationship among…

Methodology · Statistics 2023-09-26 Ksheera Sagar , Jyotishka Datta , Sayantan Banerjee , Anindya Bhadra

We focus on analyzing the classical stochastic projected gradient methods under a general dependent data sampling scheme for constrained smooth nonconvex optimization. We show the worst-case rate of convergence $\tilde{O}(t^{-1/4})$ and…

Optimization and Control · Mathematics 2023-06-26 Ahmet Alacaoglu , Hanbaek Lyu
‹ Prev 1 3 4 5 6 7 10 Next ›