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05/02/2023
Sequence Modeling with Multiresolution Convolutional Memory
Efficiently capturing the long-range patterns in sequential data sources...
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04/27/2023
Learning Absorption Rates in Glucose-Insulin Dynamics from Meal Covariates
Traditional models of glucose-insulin dynamics rely on heuristic paramet...
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05/05/2021
Granger Causality: A Review and Recent Advances
Introduced more than a half century ago, Granger causality has become a ...
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04/25/2021
Model-based metrics: Sample-efficient estimates of predictive model subpopulation performance
Machine learning models - now commonly developed to screen, diagnose, or...
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04/25/2021
Breiman's two cultures: You don't have to choose sides
Breiman's classic paper casts data analysis as a choice between two cult...
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11/30/2020
Representing and Denoising Wearable ECG Recordings
Modern wearable devices are embedded with a range of noninvasive biomark...
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11/13/2019
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...
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05/17/2019
Adaptively Truncating Backpropagation Through Time to Control Gradient Bias
Truncated backpropagation through time (TBPTT) is a popular method for l...
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01/29/2019
Stochastic Gradient MCMC for Nonlinear State Space Models
State space models (SSMs) provide a flexible framework for modeling comp...
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10/22/2018
Stochastic Gradient MCMC for State Space Models
State space models (SSMs) are a flexible approach to modeling complex ti...
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07/19/2018
Approximate Collapsed Gibbs Clustering with Expectation Propagation
We develop a framework for approximating collapsed Gibbs sampling in gen...
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06/24/2018
Disentangled VAE Representations for Multi-Aspect and Missing Data
Many problems in machine learning and related application areas are fund...
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06/19/2018
Large-Scale Stochastic Sampling from the Probability Simplex
Stochastic gradient Markov chain Monte Carlo (SGMCMC) has become a popul...
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11/22/2017
An Efficient ADMM Algorithm for Structural Break Detection in Multivariate Time Series
We present an efficient alternating direction method of multipliers (ADM...
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11/22/2017
An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality Discovery
While most classical approaches to Granger causality detection repose up...
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10/02/2017
sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo
This paper introduces the R package sgmcmc; which can be used for Bayesi...
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06/16/2017
Control Variates for Stochastic Gradient MCMC
It is well known that Markov chain Monte Carlo (MCMC) methods scale poor...
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06/14/2017
Stochastic Gradient MCMC Methods for Hidden Markov Models
Stochastic gradient MCMC (SG-MCMC) algorithms have proven useful in scal...
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06/15/2015
A Complete Recipe for Stochastic Gradient MCMC
Many recent Markov chain Monte Carlo (MCMC) samplers leverage continuous...
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05/05/2015
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...
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12/01/2014
Streaming Variational Inference for Bayesian Nonparametric Mixture Models
In theory, Bayesian nonparametric (BNP) models are well suited to stream...
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11/06/2014
Stochastic Variational Inference for Hidden Markov Models
Variational inference algorithms have proven successful for Bayesian ana...
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02/27/2014
Modeling the Complex Dynamics and Changing Correlations of Epileptic Events
Patients with epilepsy can manifest short, sub-clinical epileptic "burst...
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02/20/2014
Learning the Parameters of Determinantal Point Process Kernels
Determinantal point processes (DPPs) are well-suited for modeling repuls...
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02/17/2014
Stochastic Gradient Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for d...
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01/06/2014
Sparse graphs using exchangeable random measures
Statistical network modeling has focused on representing the graph as a ...
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11/12/2013
Approximate Inference in Continuous Determinantal Point Processes
Determinantal point processes (DPPs) are random point processes well-sui...
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09/13/2013
Mixed Membership Models for Time Series
In this article we discuss some of the consequences of the mixed members...
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08/22/2013
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...
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10/16/2012
Markov Determinantal Point Processes
A determinantal point process (DPP) is a random process useful for model...
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09/05/2012
Multiresolution Gaussian Processes
We propose a multiresolution Gaussian process to capture long-range, non...
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04/11/2012
Concept Modeling with Superwords
In information retrieval, a fundamental goal is to transform a document ...
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11/17/2011