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Found 13 papers, 4 papers with code
Breiman's two cultures: You don't have to choose sides
And while these categories represent extreme points in model space, modern computational and algorithmic tools enable us to interpolate between these points, producing flexible, interpretable, and scientifically-informed hybrids that can enjoy accurate and robust predictions, and resolve issues with data analysis that Breiman describes, such as the Rashomon effect and Occam's dilemma.
Learning Insulin-Glucose Dynamics in the Wild
We develop a new model of insulin-glucose dynamics for forecasting blood glucose in type 1 diabetics.
Adaptively Truncating Backpropagation Through Time to Control Gradient Bias
Truncated backpropagation through time (TBPTT) is a popular method for learning in recurrent neural networks (RNNs) that saves computation and memory at the cost of bias by truncating backpropagation after a fixed number of lags.
Stochastic Gradient MCMC for State Space Models
However, inference in SSMs is often computationally prohibitive for long time series.
oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis
Deep generative models have recently yielded encouraging results in producing subjectively realistic samples of complex data.
Disentangled VAE Representations for Multi-Aspect and Missing Data
Many problems in machine learning and related application areas are fundamentally variants of conditional modeling and sampling across multi-aspect data, either multi-view, multi-modal, or simply multi-group.
An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality Discovery
A sufficient condition for Granger non-causality in this setting is that all of the outgoing weights of the input data, the past lags of a series, to the first hidden layer are zero.
The Cultural Evolution of National Constitutions
We explore how ideas from infectious disease and genetics can be used to uncover patterns of cultural inheritance and innovation in a corpus of 591 national constitutions spanning 1789 - 2008.
Stochastic Gradient MCMC Methods for Hidden Markov Models
Stochastic gradient MCMC (SG-MCMC) algorithms have proven useful in scaling Bayesian inference to large datasets under an assumption of i. i. d data.
Reducing Reparameterization Gradient Variance
Optimization with noisy gradients has become ubiquitous in statistics and machine learning.
Streaming Variational Inference for Bayesian Nonparametric Mixture Models
In theory, Bayesian nonparametric (BNP) models are well suited to streaming data scenarios due to their ability to adapt model complexity with the observed data.
Stochastic Variational Inference for Hidden Markov Models
Variational inference algorithms have proven successful for Bayesian analysis in large data settings, with recent advances using stochastic variational inference (SVI).
A survey of non-exchangeable priors for Bayesian nonparametric models
Dependent nonparametric processes extend distributions over measures, such as the Dirichlet process and the beta process, to give distributions over collections of measures, typically indexed by values in some covariate space.
