arXiv research

Gaussian BP converges geometrically for pairwise linear Gaussian models.

Flexible model predicts sports outcomes over time.

Exact simulation of correlated binary outcomes using PMF constraints and linear programming.

Bayesian active learning finds individual's most preferred choice with deep Gaussian processes.

Generative model for hypergraphs captures complex interactions without pairwise reductions.

Develops new oracle inequalities for Gaussian ranking estimators.

New model accounts for scale variation and noise in pairwise comparisons.

A fast algorithm speeds up training of pairwise kernels.

A new method for binary ICA using non-stationary sources.

This paper improves decision support in multi-objective planning by better eliciting user preferences.

Hybrid-MST improves preference aggregation from sparse data.

We adapt a Markov Random Field learning algorithm for continuous variables.

Paper calculates KL divergence for isotropic Gaussian-Markov fields.

Study learns linear utility functions from comparisons, showing learnability gaps between passive and active learning.

In this paper, we consider the multivariate Bernoulli distribution as a model to estimate the structure of graphs with binary nodes. This distribution is discussed in the framework of the exponential family, and its statistical properties regarding independence of the nodes are demonstrated. Importantly the model can e…

Proposes a sparse linear classifier for classification with pairwise dependencies.

New algorithms improve robust estimation in contaminated Gaussian models.

Estimates mixture entropy using pairwise distances.

We consider the problem of learning the structure of a pairwise graphical model over continuous and discrete variables. We present a new pairwise model for graphical models with both continuous and discrete variables that is amenable to structure learning. In previous work, authors have considered structure learning of…

EM algorithm achieves optimal sample complexity for well-separated Gaussian mixtures.

We study the theoretical properties of learning a dictionary from $N$ signals $\mathbf x_i\in \mathbb R^K$ for $i=1,...,N$ via $l_1$-minimization. We assume that $\mathbf x_i$'s are $i.i.d.$ random linear combinations of the $K$ columns from a complete (i.e., square and invertible) reference dictionary $\mathbf D_0 \in…

Gaussian processes have been successful in both supervised and unsupervised machine learning tasks, but their computational complexity has constrained practical applications. We introduce a new approximation for large-scale Gaussian processes, the Gaussian Process Random Field (GPRF), in which local GPs are coupled via…

Lower bounds show learning mixtures of linear classifiers is nearly impossible.

Label assignment problems with large state spaces are important tasks especially in computer vision. Often the pairwise interaction (or smoothness prior) between labels assigned at adjacent nodes (or pixels) can be described as a function of the label difference. Exact inference in such labeling tasks is still difficul…

Dynamic Vine Copulas detect and quantify time-varying higher-order interactions in multivariate systems.

Improved score matching for non-negative data models.

Paper improves PBO using Skew Gaussian Processes for better optimization.

A new method learns from pairwise comparisons to predict sensitive data without making strong assumptions.

Improved sample complexity for Gaussian Mixture Models using Pair Correlation Factor.

We investigate different ways of generating approximate solutions to the pairwise Markov random field (MRF) selection problem. We focus mainly on the inverse Ising problem, but discuss also the somewhat related inverse Gaussian problem because both types of MRF are suitable for inference tasks with the belief propagati…

This paper studies generalization in machine learning with mixture data.

A new factor analysis method using ICA reduces portfolio concentration and diversifies excess kurtosis.

Kernel trick speeds up Bayesian interaction discovery.

Estimates signals from a continuous dictionary with sparse mixtures using optimization.

Proposes SGM for modeling complex dependencies in high-dimensional systems.

Combines two graph models to handle interference effects in Gaussian distributions.

It is a key to construct a similarity graph in graph-oriented subspace learning and clustering. In a similarity graph, each vertex denotes a data point and the edge weight represents the similarity between two points. There are two popular schemes to construct a similarity graph, i.e., pairwise distance based scheme an…

A scalable and regularized approach to minimize negative transfer in multivariate Gaussian processes.

All too often measuring statistical dependencies between financial time series is reduced to a linear correlation coefficient. However this may not capture all facets of reality. We study empirical dependencies of daily stock returns by their pairwise copulas. Here we investigate particularly to which extent the non-st…

Paper tackles learning mixture of RUMs from partial data.

We address the problem of computing approximate marginals in Gaussian probabilistic models by using mean field and fractional Bethe approximations. We define the Gaussian fractional Bethe free energy in terms of the moment parameters of the approximate marginals, derive a lower and an upper bound on the fractional Beth…

EM algorithm converges linearly and achieves sharp rate in estimating mixtures of pairwise differences.

Elicit performance metrics from classifier comparisons.

Given a set of pairwise comparisons, the classical ranking problem computes a single ranking that best represents the preferences of all users. In this paper, we study the problem of inferring individual preferences, arising in the context of making personalized recommendations. In particular, we assume that there are …

Several methods have been recently proposed for estimating sparse Gaussian graphical models using $\ell_{1}$ regularization on the inverse covariance matrix. Despite recent advances, contemporary applications require methods that are even faster in order to handle ill-conditioned high dimensional modern day datasets. I…

A Markov Chain approach for aligning generative models from pairwise human preferences.

In latent Gaussian trees the pairwise correlation signs between the variables are intrinsically unrecoverable. Such information is vital since it completely determines the direction in which two variables are associated. In this work, we resort to information theoretical approaches to achieve two fundamental goals: Fir…

Methodology to measure non-linear correlations using copulas and clustering.