
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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Learning InsulinGlucose Dynamics in the Wild
We develop a new model of insulinglucose dynamics for forecasting blood...
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Adaptively Truncating Backpropagation Through Time to Control Gradient Bias
Truncated backpropagation through time (TBPTT) is a popular method for l...
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Stochastic Gradient MCMC for State Space Models
State space models (SSMs) are a flexible approach to modeling complex ti...
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Disentangled VAE Representations for MultiAspect and Missing Data
Many problems in machine learning and related application areas are fund...
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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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The Cultural Evolution of National Constitutions
We explore how ideas from infectious disease and genetics can be used to...
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Stochastic Gradient MCMC Methods for Hidden Markov Models
Stochastic gradient MCMC (SGMCMC) algorithms have proven useful in scal...
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Reducing Reparameterization Gradient Variance
Optimization with noisy gradients has become ubiquitous in statistics an...
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Streaming Variational Inference for Bayesian Nonparametric Mixture Models
In theory, Bayesian nonparametric (BNP) models are well suited to stream...
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Stochastic Variational Inference for Hidden Markov Models
Variational inference algorithms have proven successful for Bayesian ana...
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A survey of nonexchangeable priors for Bayesian nonparametric models
Dependent nonparametric processes extend distributions over measures, su...
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A unifying representation for a class of dependent random measures
We present a general construction for dependent random measures based on...
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Nicholas J. Foti
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