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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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08/06/2020
Learning Insulin-Glucose Dynamics in the Wild
We develop a new model of insulin-glucose dynamics for forecasting blood...
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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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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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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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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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11/18/2017
The Cultural Evolution of National Constitutions
We explore how ideas from infectious disease and genetics can be used to...
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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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05/22/2017
Reducing Reparameterization Gradient Variance
Optimization with noisy gradients has become ubiquitous in statistics an...
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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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11/20/2012
A survey of non-exchangeable priors for Bayesian nonparametric models
Dependent nonparametric processes extend distributions over measures, su...
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11/20/2012
