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Emily B. Fox

Orcid: 0000-0003-3188-9685

Affiliations:

University of Washington, Department of Statistics

According to our database¹, Emily B. Fox authored at least 61 papers between 2006 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Links

Online presence:

On csauthors.net:

Bibliography

2024

Automated Statistical Model Discovery with Language Models.

[BibT_eX]

[DOI]

,

,

Noah D. Goodman

CoRR, 2024

Hybrid Square Neural ODE Causal Modeling.

[BibT_eX]

[DOI]

,

Matthew E. Levine

,

Dessi P. Zaharieva

,

,

CoRR, 2024

2023

Learning Absorption Rates in Glucose-Insulin Dynamics from Meal Covariates.

[BibT_eX]

[DOI]

Ke Alexander Wang

,

Matthew E. Levine

,

,

CoRR, 2023

Interpretable Mechanistic Representations for Meal-level Glycemic Control in the Wild.

[BibT_eX]

[DOI]

Ke Alexander Wang

,

Proceedings of the Machine Learning for Health, 2023

Sequence Modeling with Multiresolution Convolutional Memory.

[BibT_eX]

[DOI]

,

Ke Alexander Wang

,

Proceedings of the International Conference on Machine Learning, 2023

2022

Neural Granger Causality.

[BibT_eX]

[DOI]

,

,

Nicholas J. Foti

,

,

IEEE Trans. Pattern Anal. Mach. Intell., 2022

2021

The Convex Mixture Distribution: Granger Causality for Categorical Time Series.

[BibT_eX]

[DOI]

,

,

,

SIAM J. Math. Data Sci., 2021

It's complicated: characterizing the time-varying relationship between cell phone mobility and COVID-19 spread in the US.

[BibT_eX]

[DOI]

,

,

,

Andrew C. Miller

,

Nicholas J. Foti

,

npj Digit. Medicine, 2021

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

[BibT_eX]

[DOI]

,

Philippe Vincent-Lamarre

,

,

Vincent Larivière

,

Alina Beygelzimer

,

Florence d'Alché-Buc

,

,

Hugo Larochelle

J. Mach. Learn. Res., 2021

Granger Causality: A Review and Recent Advances.

[BibT_eX]

[DOI]

,

CoRR, 2021

Breiman's two cultures: You don't have to choose sides.

[BibT_eX]

[DOI]

Andrew C. Miller

,

Nicholas J. Foti

,

CoRR, 2021

Model-based metrics: Sample-efficient estimates of predictive model subpopulation performance.

[BibT_eX]

[DOI]

Andrew C. Miller

,

,

,

Proceedings of the Machine Learning for Healthcare Conference, 2021

2020

Representing and Denoising Wearable ECG Recordings.

[BibT_eX]

[DOI]

,

Andrew C. Miller

,

CoRR, 2020

Learning Insulin-Glucose Dynamics in the Wild.

[BibT_eX]

[DOI]

Andrew C. Miller

,

Nicholas J. Foti

,

Proceedings of the Machine Learning for Healthcare Conference, 2020

2019

Stochastic Gradient MCMC for State Space Models.

[BibT_eX]

[DOI]

Christopher Aicher

,

,

Nicholas J. Foti

,

SIAM J. Math. Data Sci., 2019

Irreversible samplers from jump and continuous Markov processes.

[BibT_eX]

[DOI]

,

,

,

Stat. Comput., 2019

Control variates for stochastic gradient MCMC.

[BibT_eX]

[DOI]

,

,

,

Christopher Nemeth

Stat. Comput., 2019

Modeling patterns of smartphone usage and their relationship to cognitive health.

[BibT_eX]

[DOI]

,

,

CoRR, 2019

Stochastic Gradient MCMC for Nonlinear State Space Models.

[BibT_eX]

[DOI]

Christopher Aicher

,

,

Christopher Nemeth

,

,

CoRR, 2019

Adaptively Truncating Backpropagation Through Time to Control Gradient Bias.

[BibT_eX]

[DOI]

Christopher Aicher

,

Nicholas J. Foti

,

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

A Simple Adaptive Tracker with Reminiscences.

[BibT_eX]

[DOI]

Christopher Xie

,

,

Zaïd Harchaoui

Proceedings of the International Conference on Robotics and Automation, 2019

2018

Approximate Collapsed Gibbs Clustering with Expectation Propagation.

[BibT_eX]

[DOI]

Christopher Aicher

,

CoRR, 2018

Disentangled VAE Representations for Multi-Aspect and Missing Data.

[BibT_eX]

[DOI]

Samuel K. Ainsworth

,

Nicholas J. Foti

,

CoRR, 2018

Interpretable VAEs for nonlinear group factor analysis.

[BibT_eX]

[DOI]

Samuel K. Ainsworth

,

Nicholas J. Foti

,

Adrian K. C. Lee

,

CoRR, 2018

Large-Scale Stochastic Sampling from the Probability Simplex.

[BibT_eX]

[DOI]

,

,

,

Christopher Nemeth

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis.

[BibT_eX]

[DOI]

Samuel K. Ainsworth

,

Nicholas J. Foti

,

Adrian K. C. Lee

,

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

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

[BibT_eX]

[DOI]

Christopher Xie

,

,

Alec Greaves-Tunnell

,

CoRR, 2017

Stochastic Gradient MCMC Methods for Hidden Markov Models.

[BibT_eX]

[DOI]

,

Nicholas J. Foti

,

Proceedings of the 34th International Conference on Machine Learning, 2017

2015

Guest Editors' Introduction to the Special Issue on Bayesian Nonparametrics.

[BibT_eX]

[DOI]

,

,

Erik B. Sudderth

,

IEEE Trans. Pattern Anal. Mach. Intell., 2015

Bayesian nonparametric covariance regression.

[BibT_eX]

[DOI]

,

David B. Dunson

J. Mach. Learn. Res., 2015

Bayesian Structure Learning for Stationary Time Series.

[BibT_eX]

[DOI]

,

Nicholas J. Foti

,

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

A Complete Recipe for Stochastic Gradient MCMC.

[BibT_eX]

[DOI]

,

,

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Streaming Variational Inference for Bayesian Nonparametric Mixture Models.

[BibT_eX]

[DOI]

,

Nicholas J. Foti

,

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Mixed Membership Models for Time Series.

[BibT_eX]

[DOI]

,

Michael I. Jordan

Proceedings of the Handbook of Mixed Membership Models and Their Applications., 2014

Bayesian nonparametric models of sparse and exchangeable random graphs.

[BibT_eX]

[DOI]

,

CoRR, 2014

Modeling the complex dynamics and changing correlations of epileptic events.

[BibT_eX]

[DOI]

Drausin F. Wulsin

,

,

Artif. Intell., 2014

Expectation-Maximization for Learning Determinantal Point Processes.

[BibT_eX]

[DOI]

Jennifer Gillenwater

,

,

,

Benjamin Taskar

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Stochastic variational inference for hidden Markov models.

[BibT_eX]

[DOI]

Nicholas J. Foti

,

,

,

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Stochastic Gradient Hamiltonian Monte Carlo.

[BibT_eX]

[DOI]

,

,

Carlos Guestrin

Proceedings of the 31th International Conference on Machine Learning, 2014

Learning the Parameters of Determinantal Point Process Kernels.

[BibT_eX]

[DOI]

Raja Hafiz Affandi

,

,

,

Benjamin Taskar

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

A Bayesian Approach for Predicting the Popularity of Tweets

[BibT_eX]

[DOI]

,

,

Eric T. Bradlow

CoRR, 2013

Mixed Membership Models for Time Series.

[BibT_eX]

[DOI]

,

Michael I. Jordan

CoRR, 2013

Approximate Inference in Continuous Determinantal Point Processes.

[BibT_eX]

[DOI]

Raja Hafiz Affandi

,

,

CoRR, 2013

Approximate Inference in Continuous Determinantal Processes.

[BibT_eX]

[DOI]

Raja Hafiz Affandi

,

,

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Representing documents through their readers.

[BibT_eX]

[DOI]

Khalid El-Arini

,

,

,

Carlos Guestrin

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

Parsing epileptic events using a Markov switching process model for correlated time series.

[BibT_eX]

[DOI]

,

,

Proceedings of the 30th International Conference on Machine Learning, 2013

Nystrom Approximation for Large-Scale Determinantal Processes.

[BibT_eX]

[DOI]

Raja Hafiz Affandi

,

,

,

Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012

Hierarchical Latent Dictionaries for Models of Brain Activation.

[BibT_eX]

[DOI]

,

,

David B. Dunson

,

Tom M. Mitchell

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Concept Modeling with Superwords

[BibT_eX]

[DOI]

Khalid El-Arini

,

,

Carlos Guestrin

CoRR, 2012

Markov Determinantal Point Processes.

[BibT_eX]

[DOI]

Raja Hafiz Affandi

,

,

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

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

[BibT_eX]

[DOI]

Michael C. Hughes

,

,

Erik B. Sudderth

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Multiresolution Gaussian Processes.

[BibT_eX]

[DOI]

,

David B. Dunson

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011

Bayesian Nonparametric Inference of Switching Dynamic Linear Models.

[BibT_eX]

[DOI]

,

Erik B. Sudderth

,

Michael I. Jordan

,

Alan S. Willsky

IEEE Trans. Signal Process., 2011

2010

Bayesian Nonparametric Methods for Learning Markov Switching Processes.

[BibT_eX]

[DOI]

,

Erik B. Sudderth

,

Michael I. Jordan

,

Alan S. Willsky

IEEE Signal Process. Mag., 2010

2009

Bayesian nonparametric learning of complex dynamical phenomena.

[BibT_eX]

[DOI]

PhD thesis, 2009

Sharing Features among Dynamical Systems with Beta Processes.

[BibT_eX]

[DOI]

,

Erik B. Sudderth

,

Michael I. Jordan

,

Alan S. Willsky

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2008

Nonparametric Bayesian Learning of Switching Linear Dynamical Systems.

[BibT_eX]

[DOI]

,

Erik B. Sudderth

,

Michael I. Jordan

,

Alan S. Willsky

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

An HDP-HMM for systems with state persistence.

[BibT_eX]

[DOI]

,

Erik B. Sudderth

,

Michael I. Jordan

,

Alan S. Willsky

Proceedings of the Machine Learning, 2008

2007

Detection and Localization of Material Releases With Sparse Sensor Configurations.

[BibT_eX]

[DOI]

,

John W. Fisher III

,

Alan S. Willsky

IEEE Trans. Signal Process., 2007

Hierarchical Dirichlet processes for tracking maneuvering targets.

[BibT_eX]

[DOI]

,

Erik B. Sudderth

,

Alan S. Willsky

Proceedings of the 10th International Conference on Information Fusion, 2007

2006

Detection and Localization of Material Releases with Sparse Sensor Configurations.

[BibT_eX]

[DOI]

,

Jason L. Williams

,

John W. Fisher III

,

Alan S. Willsky

Proceedings of the 2006 IEEE International Conference on Acoustics Speech and Signal Processing, 2006

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