
Stochastic Gradient MCMC for Nonlinear State Space Models
State space models (SSMs) provide a flexible framework for modeling comp...
01/29/2019 ∙ by Christopher Aicher, et al. ∙ 12 ∙ shareread it

An Efficient ADMM Algorithm for Structural Break Detection in Multivariate Time Series
We present an efficient alternating direction method of multipliers (ADM...
11/22/2017 ∙ by Alex Tank, et al. ∙ 0 ∙ shareread it

An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality Discovery
While most classical approaches to Granger causality detection repose up...
11/22/2017 ∙ by Alex Tank, et al. ∙ 0 ∙ shareread it

Control Variates for Stochastic Gradient MCMC
It is well known that Markov chain Monte Carlo (MCMC) methods scale poor...
06/16/2017 ∙ by Jack Baker, et al. ∙ 0 ∙ shareread it

Stochastic Gradient MCMC Methods for Hidden Markov Models
Stochastic gradient MCMC (SGMCMC) algorithms have proven useful in scal...
06/14/2017 ∙ by Yian Ma, et al. ∙ 0 ∙ shareread it

A Complete Recipe for Stochastic Gradient MCMC
Many recent Markov chain Monte Carlo (MCMC) samplers leverage continuous...
06/15/2015 ∙ by Yian Ma, et al. ∙ 0 ∙ shareread it

Achieving a Hyperlocal Housing Price Index: Overcoming Data Sparsity by Bayesian Dynamical Modeling of Multiple Data Streams
Understanding how housing values evolve over time is important to policy...
05/05/2015 ∙ by You Ren, et al. ∙ 0 ∙ shareread it

Streaming Variational Inference for Bayesian Nonparametric Mixture Models
In theory, Bayesian nonparametric (BNP) models are well suited to stream...
12/01/2014 ∙ by Alex Tank, et al. ∙ 0 ∙ shareread it

Stochastic Variational Inference for Hidden Markov Models
Variational inference algorithms have proven successful for Bayesian ana...
11/06/2014 ∙ by Nicholas J. Foti, et al. ∙ 0 ∙ shareread it

Modeling the Complex Dynamics and Changing Correlations of Epileptic Events
Patients with epilepsy can manifest short, subclinical epileptic "burst...
02/27/2014 ∙ by Drausin F. Wulsin, et al. ∙ 0 ∙ shareread it

Learning the Parameters of Determinantal Point Process Kernels
Determinantal point processes (DPPs) are wellsuited for modeling repuls...
02/20/2014 ∙ by Raja Hafiz Affandi, et al. ∙ 0 ∙ shareread it

Stochastic Gradient Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for d...
02/17/2014 ∙ by Tianqi Chen, et al. ∙ 0 ∙ shareread it

Sparse graphs using exchangeable random measures
Statistical network modeling has focused on representing the graph as a ...
01/06/2014 ∙ by François Caron, et al. ∙ 0 ∙ shareread it

Approximate Inference in Continuous Determinantal Point Processes
Determinantal point processes (DPPs) are random point processes wellsui...
11/12/2013 ∙ by Raja Hafiz Affandi, et al. ∙ 0 ∙ shareread it

Mixed Membership Models for Time Series
In this article we discuss some of the consequences of the mixed members...
09/13/2013 ∙ by Emily B. Fox, et al. ∙ 0 ∙ shareread it

Joint modeling of multiple time series via the beta process with application to motion capture segmentation
We propose a Bayesian nonparametric approach to the problem of jointly m...
08/22/2013 ∙ by Emily B. Fox, et al. ∙ 0 ∙ shareread it

Markov Determinantal Point Processes
A determinantal point process (DPP) is a random process useful for model...
10/16/2012 ∙ by Raja Hafiz Affandi, et al. ∙ 0 ∙ shareread it

Multiresolution Gaussian Processes
We propose a multiresolution Gaussian process to capture longrange, non...
09/05/2012 ∙ by Emily B. Fox, et al. ∙ 0 ∙ shareread it

Concept Modeling with Superwords
In information retrieval, a fundamental goal is to transform a document ...
04/11/2012 ∙ by Khalid ElArini, et al. ∙ 0 ∙ shareread it

Joint Modeling of Multiple Related Time Series via the Beta Process
We propose a Bayesian nonparametric approach to the problem of jointly m...
11/17/2011 ∙ by Emily B. Fox, et al. ∙ 0 ∙ shareread it

sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo
This paper introduces the R package sgmcmc; which can be used for Bayesi...
10/02/2017 ∙ by Jack Baker, et al. ∙ 0 ∙ shareread it

Disentangled VAE Representations for MultiAspect and Missing Data
Many problems in machine learning and related application areas are fund...
06/24/2018 ∙ by Samuel K. Ainsworth, et al. ∙ 0 ∙ shareread it

LargeScale Stochastic Sampling from the Probability Simplex
Stochastic gradient Markov chain Monte Carlo (SGMCMC) has become a popul...
06/19/2018 ∙ by Jack Baker, et al. ∙ 0 ∙ shareread it

Approximate Collapsed Gibbs Clustering with Expectation Propagation
We develop a framework for approximating collapsed Gibbs sampling in gen...
07/19/2018 ∙ by Christopher Aicher, et al. ∙ 0 ∙ shareread it

Stochastic Gradient MCMC for State Space Models
State space models (SSMs) are a flexible approach to modeling complex ti...
10/22/2018 ∙ by Christopher Aicher, et al. ∙ 0 ∙ shareread it

Adaptively Truncating Backpropagation Through Time to Control Gradient Bias
Truncated backpropagation through time (TBPTT) is a popular method for l...
05/17/2019 ∙ by Christopher Aicher, et al. ∙ 0 ∙ shareread it

Modeling patterns of smartphone usage and their relationship to cognitive health
The ubiquity of smartphone usage in many people's lives make it a rich s...
11/13/2019 ∙ by Jonas Rauber, et al. ∙ 0 ∙ shareread it
Emily B. Fox
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Associate Professor in the Paul G. Allen School of Computer Science & Engineering and Department of Statistics at the University of Washington