arXiv research

Unified approach to linear regression using covariance fitting for optimal weights.

There has been a lot of work fitting Ising models to multivariate binary data in order to understand the conditional dependency relationships between the variables. However, additional covariates are frequently recorded together with the binary data, and may influence the dependence relationships. Motivated by such a d…

Improved estimators for causal inference using cross-fitting and undersmoothing.

New method for estimating covariance with robustness to outliers.

Latent space models are effective tools for statistical modeling and exploration of network data. These models can effectively model real world network characteristics such as degree heterogeneity, transitivity, homophily, etc. Due to their close connection to generalized linear models, it is also natural to incorporat…

Method estimates multiple related Gaussian distributions using Laplacian regularization.

Standard sparse pseudo-input approximations to the Gaussian process (GP) cannot handle complex functions well. Sparse spectrum alternatives attempt to answer this but are known to over-fit. We suggest the use of variational inference for the sparse spectrum approximation to avoid both issues. We model the covariance fu…

New GMM models fit high-dimensional data with fewer parameters.

New CLT for AIPW estimator in high-dimensional settings.

In this paper, we propose a new framework to remove parts of the systematic errors affecting popular restoration algorithms, with a special focus for image processing tasks. Generalizing ideas that emerged for $\ell_1$ regularization, we develop an approach re-fitting the results of standard methods towards the input d…

We speed up Gaussian process cross-validation calculations and improve model diagnostics.

Develops a test for conditional local independence of counting processes.

Community detection or clustering is a fundamental task in the analysis of network data. Many real networks have a bipartite structure which makes community detection challenging. In this paper, we consider a model which allows for matched communities in the bipartite setting, in addition to node covariates with inform…

Predict covariance from features using convex optimization.

CNN improves causal inference by controlling time-structured covariates.

T-Rex uses EM to fit robust factor models in noisy data.

Gaussian Processes offer a flexible method for modeling and predicting outcomes with uncertainty estimates.

Iterative methods for fitting a Gaussian Random Field (GRF) model via maximum likelihood (ML) estimation requires solving a nonconvex optimization problem. The problem is aggravated for anisotropic GRFs where the number of covariance function parameters increases with the dimension. Even evaluation of the likelihood fu…

Unified framework for debiased machine learning using Riesz representer and Bregman divergence.

FuncNN package enables deep learning with functional covariates.

Proposes an L1-regularized functional SVM for binary classification with functional covariates.

Method uses random forest with distance covariance for transfer learning in healthcare.

A new multi-task learning estimator improves Gaussian graphical regression model fitting.

In this contribution we describe an approach to evolve composite covariance functions for Gaussian processes using genetic programming. A critical aspect of Gaussian processes and similar kernel-based models such as SVM is, that the covariance function should be adapted to the modeled data. Frequently, the squared expo…

Study semi-supervised learning with noisy proxy covariates, deriving bounds and showing gains.

Linear and Quadratic Discriminant analysis (LDA/QDA) are common tools for classification problems. For these methods we assume observations are normally distributed within group. We estimate a mean and covariance matrix for each group and classify using Bayes theorem. With LDA, we estimate a single, pooled covariance m…

Sliced inverse regression is a popular tool for sufficient dimension reduction, which replaces covariates with a minimal set of their linear combinations without loss of information on the conditional distribution of the response given the covariates. The estimated linear combinations include all covariates, making res…

The paper improves ranking by integrating covariates and sparse intrinsic scores.

Develops a fast algorithm for fitting multilevel factor models.

A new ranking model with dynamic covariates improves statistical analysis.

We study differential cohomology on categories of globally hyperbolic Lorentzian manifolds. The Lorentzian metric allows us to define a natural transformation whose kernel generalizes Maxwell's equations and fits into a restriction of the fundamental exact sequences of differential cohomology. We consider smooth Pontry…

We present a new algorithm for boosting generalized additive models for location, scale and shape (GAMLSS) that allows to incorporate stability selection, an increasingly popular way to obtain stable sets of covariates while controlling the per-family error rate (PFER). The model is fitted repeatedly to subsampled data…

Efficiently private regression for unbounded data.

Fitting high-dimensional data involves a delicate tradeoff between faithful representation and the use of sparse models. Too often, sparsity assumptions on the fitted model are too restrictive to provide a faithful representation of the observed data. In this paper, we present a novel framework incorporating sparsity i…

The Extreme Deconvolution method fits a probability density to a dataset where each observation has Gaussian noise added with a known sample-specific covariance, originally intended for use with astronomical datasets. The existing fitting method is batch EM, which would not normally be applied to large datasets such as…

CovNet models covariance for multidimensional functional data efficiently.

Building on the Utiyama principle we formulate an approach to Lagrangian field theory in which exterior covariant differentials of vector-valued forms replace partial derivatives, in the sense that they take up the role played by the latter in the usual jet bundle formulation. Actually a natural Lagrangian can be writt…

Improved algorithm for conditional linear regression with heterogeneous covariances.

Study predictive performance of linear regression with random functional covariates.

CRUMB: Efficient Prior Fitted Network Inference via Distributionally Matched Context Batching

Neural network method estimates covariate-dependent graphical models with statistical guarantees.

The exact meaning of the noise spectrum of eigenvalues of the covariance matrix is discussed. In order to better understand the possible phenomena behind the observed noise, the spectrum of eigenvalues of the covariance matrix is studied under a model where most of the true eigenvalues are zero and the parameters are n…

Proposes a new estimator for weak instrumental variables in panel data models.

TILT improves target domain performance by penalizing an auxiliary component on unlabeled target inputs.

The accurate prediction of time-changing covariances is an important problem in the modeling of multivariate financial data. However, some of the most popular models suffer from a) overfitting problems and multiple local optima, b) failure to capture shifts in market conditions and c) large computational costs. To addr…

Improved Kalman filtering with hierarchical variational approach.

Extends Infinitesimal Jackknife for model covariance, enhancing ensemble model analysis.

We consider the problem of estimating the mean and covariance of a distribution from iid samples in $\mathbb{R}^n$, in the presence of an $η$ fraction of malicious noise; this is in contrast to much recent work where the noise itself is assumed to be from a distribution of known type. The agnostic problem includes many…