Emily B. Fox

According to our database1, Emily B. Fox authored at least 53 papers between 2006 and 2019.

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Bibliography

2019
Irreversible samplers from jump and continuous Markov processes.
Statistics and Computing, 2019

Control variates for stochastic gradient MCMC.
Statistics and Computing, 2019

Adaptively Truncating Backpropagation Through Time to Control Gradient Bias.
CoRR, 2019

Stochastic Gradient MCMC for Nonlinear State Space Models.
CoRR, 2019

Adaptively Truncating Backpropagation Through Time to Control Gradient Bias.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

A Simple Adaptive Tracker with Reminiscences.
Proceedings of the International Conference on Robotics and Automation, 2019

2018
Stochastic Gradient MCMC for State Space Models.
CoRR, 2018

Approximate Collapsed Gibbs Clustering with Expectation Propagation.
CoRR, 2018

Disentangled VAE Representations for Multi-Aspect and Missing Data.
CoRR, 2018

Large-Scale Stochastic Sampling from the Probability Simplex.
CoRR, 2018

Interpretable VAEs for nonlinear group factor analysis.
CoRR, 2018

Large-Scale Stochastic Sampling from the Probability Simplex.
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.
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.
CoRR, 2017

Control Variates for Stochastic Gradient MCMC.
CoRR, 2017

Stochastic Gradient MCMC Methods for Hidden Markov Models.
Proceedings of the 34th International Conference on Machine Learning, 2017

2015
Guest Editors' Introduction to the Special Issue on Bayesian Nonparametrics.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

Bayesian nonparametric covariance regression.
J. Mach. Learn. Res., 2015

Bayesian Structure Learning for Stationary Time Series.
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

A Complete Recipe for Stochastic Gradient MCMC.
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.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

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

Expectation-Maximization for Learning Determinantal Point Processes.
CoRR, 2014

Stochastic Gradient Hamiltonian Monte Carlo.
CoRR, 2014

Bayesian nonparametric models of sparse and exchangeable random graphs.
CoRR, 2014

Learning the Parameters of Determinantal Point Process Kernels.
CoRR, 2014

Modeling the complex dynamics and changing correlations of epileptic events.
Artif. Intell., 2014

Expectation-Maximization for Learning Determinantal Point Processes.
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.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Stochastic Gradient Hamiltonian Monte Carlo.
Proceedings of the 31th International Conference on Machine Learning, 2014

Learning the Parameters of Determinantal Point Process Kernels.
Proceedings of the 31th International Conference on Machine Learning, 2014

2013
A Bayesian Approach for Predicting the Popularity of Tweets
CoRR, 2013

Mixed Membership Models for Time Series.
CoRR, 2013

Approximate Inference in Continuous Determinantal Point Processes.
CoRR, 2013

Approximate Inference in Continuous Determinantal Processes.
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.
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.
Proceedings of the 30th International Conference on Machine Learning, 2013

Nystrom Approximation for Large-Scale Determinantal Processes.
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012
Hierarchical Latent Dictionaries for Models of Brain Activation.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Markov Determinantal Point Processes
CoRR, 2012

Concept Modeling with Superwords
CoRR, 2012

Markov Determinantal Point Processes.
Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data.
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.
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.
IEEE Trans. Signal Processing, 2011

2010
Bayesian Nonparametric Methods for Learning Markov Switching Processes.
IEEE Signal Process. Mag., 2010

2009
Bayesian nonparametric learning of complex dynamical phenomena.
PhD thesis, 2009

Sharing Features among Dynamical Systems with Beta Processes.
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.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

An HDP-HMM for systems with state persistence.
Proceedings of the Machine Learning, 2008

2007
Detection and Localization of Material Releases With Sparse Sensor Configurations.
IEEE Trans. Signal Processing, 2007

Hierarchical Dirichlet processes for tracking maneuvering targets.
Proceedings of the 10th International Conference on Information Fusion, 2007

2006
Detection and Localization of Material Releases with Sparse Sensor Configurations.
Proceedings of the 2006 IEEE International Conference on Acoustics Speech and Signal Processing, 2006


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