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.
no code implementations • 27 Mar 2020 • Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d'Alché-Buc, Emily Fox, Hugo Larochelle
Reproducibility, that is obtaining similar results as presented in a paper or talk, using the same code and data (when available), is a necessary step to verify the reliability of research findings.
no code implementations • 17 Feb 2018 • Samuel Ainsworth, Nicholas Foti, Adrian KC Lee, Emily Fox
Deep generative models have recently yielded encouraging results in producing subjectively realistic samples of complex data.
3 code implementations • 16 Feb 2018 • Alex Tank, Ian Covert, Nicholas Foti, Ali Shojaie, Emily Fox
We show that our neural Granger causality methods outperform state-of-the-art nonlinear Granger causality methods on the DREAM3 challenge data.
no code implementations • 23 Oct 2017 • Christopher Xie, Alex Tank, Alec Greaves-Tunnell, Emily Fox
Providing long-range forecasts is a fundamental challenge in time series modeling, which is only compounded by the challenge of having to form such forecasts when a time series has never previously been observed.
no code implementations • NeurIPS 2014 • Jennifer Gillenwater, Alex Kulesza, Emily Fox, Ben Taskar
However, log-likelihood is non-convex in the entries of the kernel matrix, and this learning problem is conjectured to be NP-hard.
no code implementations • NeurIPS 2013 • Raja Hafiz Affandi, Emily Fox, Ben Taskar
Determinantal point processes (DPPs) are random point processes well-suited for modeling repulsion.
no code implementations • NeurIPS 2012 • Emily Fox, David B. Dunson
We propose a multiresolution Gaussian process to capture long-range, non-Markovian dependencies while allowing for abrupt changes.
no code implementations • NeurIPS 2012 • Michael C. Hughes, Emily Fox, Erik B. Sudderth
Applications of Bayesian nonparametric methods require learning and inference algorithms which efficiently explore models of unbounded complexity.
no code implementations • NeurIPS 2009 • Emily Fox, Michael. I. Jordan, Erik B. Sudderth, Alan S. Willsky
We propose a Bayesian nonparametric approach to relating multiple time series via a set of latent, dynamical behaviors.
no code implementations • NeurIPS 2008 • Emily Fox, Erik B. Sudderth, Michael. I. Jordan, Alan S. Willsky
Many nonlinear dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes.