Greedy Gaussian segmentation of multivariate time series
We consider the problem of breaking a multivariate (vector) time series into segments over
which the data is well explained as independent samples from a Gaussian distribution. We …
which the data is well explained as independent samples from a Gaussian distribution. We …
A computationally efficient method for learning exponential family distributions
We consider the question of learning the natural parameters of a $ k $ parameter\textit
{minimal} exponential family from iid samples in a computationally and statistically efficient …
{minimal} exponential family from iid samples in a computationally and statistically efficient …
[HTML][HTML] Graphical models for zero-inflated single cell gene expression
Bulk gene expression experiments relied on aggregations of thousands of cells to measure
the average expression in an organism. Advances in microfluidic and droplet sequencing …
the average expression in an organism. Advances in microfluidic and droplet sequencing …
On learning continuous pairwise markov random fields
We consider learning a sparse pairwise Markov Random Field (MRF) with continuous-
valued variables from iid samples. We adapt the algorithm of Vuffray et al.(2019) to this …
valued variables from iid samples. We adapt the algorithm of Vuffray et al.(2019) to this …
On Computationally Efficient Learning of Exponential Family Distributions
We consider the classical problem of learning, with arbitrary accuracy, the natural
parameters of a $ k $-parameter truncated\textit {minimal} exponential family from iid …
parameters of a $ k $-parameter truncated\textit {minimal} exponential family from iid …
Square root graphical models: Multivariate generalizations of univariate exponential families that permit positive dependencies
D Inouye, P Ravikumar… - … conference on machine …, 2016 - proceedings.mlr.press
Abstract We develop Square Root Graphical Models (SQR), a novel class of parametric
graphical models that provides multivariate generalizations of univariate exponential family …
graphical models that provides multivariate generalizations of univariate exponential family …
Learning the network structure of heterogeneous data via pairwise exponential Markov random fields
Markov random fields (MRFs) are a useful tool for modeling relationships present in large
and high-dimensional data. Often, this data comes from various sources and can have …
and high-dimensional data. Often, this data comes from various sources and can have …
Learning continuous exponential families beyond gaussian
We address the problem of learning of continuous exponential family distributions with
unbounded support. While a lot of progress has been made on learning of Gaussian …
unbounded support. While a lot of progress has been made on learning of Gaussian …
Ordinal graphical models: A tale of two approaches
AS Suggala, E Yang… - … conference on machine …, 2017 - proceedings.mlr.press
Undirected graphical models or Markov random fields (MRFs) are widely used for modeling
multivariate probability distributions. Much of the work on MRFs has focused on continuous …
multivariate probability distributions. Much of the work on MRFs has focused on continuous …
A Unified Theory of Exact Inference and Learning in Exponential Family Latent Variable Models
S Sokoloski - arXiv preprint arXiv:2404.19501, 2024 - arxiv.org
Bayes' rule describes how to infer posterior beliefs about latent variables given
observations, and inference is a critical step in learning algorithms for latent variable models …
observations, and inference is a critical step in learning algorithms for latent variable models …