no code implementations • 25 Apr 2021 • Andrew C. Miller, Nicholas J. Foti, Emily B. Fox
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.
no code implementations • 6 Aug 2020 • Andrew C. Miller, Nicholas J. Foti, Emily Fox
We develop a new model of insulin-glucose dynamics for forecasting blood glucose in type 1 diabetics.
1 code implementation • 17 May 2019 • Christopher Aicher, Nicholas J. Foti, Emily B. Fox
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.
1 code implementation • 22 Oct 2018 • Christopher Aicher, Yi-An Ma, Nicholas J. Foti, Emily B. Fox
However, inference in SSMs is often computationally prohibitive for long time series.
no code implementations • ICML 2018 • Samuel K. Ainsworth, Nicholas J. Foti, Adrian K. C. Lee, Emily B. Fox
Deep generative models have recently yielded encouraging results in producing subjectively realistic samples of complex data.
no code implementations • 24 Jun 2018 • 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-modal, or simply multi-group.
1 code implementation • 22 Nov 2017 • Alex Tank, Ian Cover, Nicholas J. Foti, Ali Shojaie, Emily B. Fox
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.
no code implementations • 18 Nov 2017 • Daniel N. Rockmore, Chen Fang, Nicholas J. Foti, Tom Ginsburg, David C. Krakauer
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.
no code implementations • ICML 2017 • Yi-An Ma, Nicholas J. Foti, Emily B. Fox
Stochastic gradient MCMC (SG-MCMC) algorithms have proven useful in scaling Bayesian inference to large datasets under an assumption of i. i. d data.
1 code implementation • NeurIPS 2017 • Andrew C. Miller, Nicholas J. Foti, Alexander D'Amour, Ryan P. Adams
Optimization with noisy gradients has become ubiquitous in statistics and machine learning.
no code implementations • 1 Dec 2014 • Alex Tank, Nicholas J. Foti, Emily B. Fox
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.
no code implementations • NeurIPS 2014 • Nicholas J. Foti, Jason Xu, Dillon Laird, Emily B. Fox
Variational inference algorithms have proven successful for Bayesian analysis in large data settings, with recent advances using stochastic variational inference (SVI).
no code implementations • 20 Nov 2012 • Nicholas J. Foti, Sinead Williamson
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.