Search Results for author: Emily Fox

Found 11 papers, 1 papers with code

Learning Insulin-Glucose Dynamics in the Wild

no code implementations6 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.

Inductive Bias

Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)

no code implementations27 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.

BIG-bench Machine Learning

Interpretable VAEs for nonlinear group factor analysis

no code implementations17 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.

Neural Granger Causality

2 code implementations16 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.

Time Series

A Unified Framework for Long Range and Cold Start Forecasting of Seasonal Profiles in Time Series

no code implementations23 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.

Recommendation Systems Time Series

Expectation-Maximization for Learning Determinantal Point Processes

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.

Point Processes Product Recommendation

Approximate Inference in Continuous Determinantal Processes

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.

Point Processes

Multiresolution Gaussian Processes

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.

Gaussian Processes

Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data

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.

Time Series

Sharing Features among Dynamical Systems with Beta Processes

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.

Time Series

Nonparametric Bayesian Learning of Switching Linear Dynamical Systems

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.

Cannot find the paper you are looking for? You can Submit a new open access paper.