469 citations · 2k across the 43 of their papers we have counts for
6 papers · 1 filter
Stochastic Gradient MCMC for State Space Models
Christopher Aicher, Yi-An Ma, Nicholas J. Foti +1
State space models (SSMs) are a flexible approach to modeling complex time series. However, inference in SSMs is often computationally prohibitive for long time series. Stochastic…
Approximate Collapsed Gibbs Clustering with Expectation Propagation
Christopher Aicher, Emily B. Fox
We develop a framework for approximating collapsed Gibbs sampling in generative latent variable cluster models. Collapsed Gibbs is a popular MCMC method, which integrates out varia…
Disentangled VAE Representations for Multi-Aspect and Missing Data
Samuel K. Ainsworth, Nicholas J. Foti, Emily B. Fox
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-m…
Large-Scale Stochastic Sampling from the Probability Simplex
Jack Baker, Paul Fearnhead, Emily B Fox +1
Stochastic gradient Markov chain Monte Carlo (SGMCMC) has become a popular method for scalable Bayesian inference. These methods are based on sampling a discrete-time approximation…
Interpretable VAEs for nonlinear group factor analysis
Samuel Ainsworth, Nicholas Foti, Adrian KC Lee +1
Deep generative models have recently yielded encouraging results in producing subjectively realistic samples of complex data. Far less attention has been paid to making these gener…
Neural Granger Causality
Alex Tank, Ian Covert, Nicholas Foti +2
While most classical approaches to Granger causality detection assume linear dynamics, many interactions in real-world applications, like neuroscience and genomics, are inherently…