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no code implementations • 5 May 2021 • Ali Shojaie, Emily B. Fox

Introduced more than a half century ago, Granger causality has become a popular tool for analyzing time series data in many application domains, from economics and finance to genomics and neuroscience.

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 • 25 Apr 2021 • Andrew C. Miller, Leon A. Gatys, Joseph Futoma, Emily B. Fox

We propose using an evaluation model $-$ a model that describes the conditional distribution of the predictive model score $-$ to form model-based metric (MBM) estimates.

no code implementations • 30 Nov 2020 • Jeffrey Chan, Andrew C. Miller, Emily B. Fox

In this work, we develop a statistical model to simulate a structured noise process in ECGs derived from a wearable sensor, design a beat-to-beat representation that is conducive for analyzing variation, and devise a factor analysis-based method to denoise the ECG.

no code implementations • 13 Nov 2019 • Jonas Rauber, Emily B. Fox, Leon A. Gatys

The ubiquity of smartphone usage in many people's lives make it a rich source of information about a person's mental and cognitive state.

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.

2 code implementations • 29 Jan 2019 • Christopher Aicher, Srshti Putcha, Christopher Nemeth, Paul Fearnhead, Emily B. Fox

The challenge is two-fold: not only do computations scale linearly with time, as in the linear case, but particle filters additionally suffer from increasing particle degeneracy with longer series.

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 • 19 Jul 2018 • Christopher Aicher, Emily B. Fox

We develop a framework for approximating collapsed Gibbs sampling in generative latent variable cluster models.

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 • NeurIPS 2018 • Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth

Unfortunately, many popular large-scale Bayesian models, such as network or topic models, require inference on sparse simplex spaces.

no code implementations • 22 Nov 2017 • Alex Tank, Emily B. Fox, Ali Shojaie

We present an efficient alternating direction method of multipliers (ADMM) algorithm for segmenting a multivariate non-stationary time series with structural breaks into stationary regions.

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.

1 code implementation • 2 Oct 2017 • Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth

To do this, the package uses the software library TensorFlow, which has a variety of statistical distributions and mathematical operations as standard, meaning a wide class of models can be built using this framework.

1 code implementation • 16 Jun 2017 • Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth

These methods use a noisy estimate of the gradient of the log posterior, which reduces the per iteration computational cost of the algorithm.

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.

no code implementations • NeurIPS 2015 • Yi-An Ma, Tianqi Chen, Emily B. Fox

That is, any continuous Markov process that provides samples from the target distribution can be written in our framework.

no code implementations • 5 May 2015 • You Ren, Emily B. Fox, Andrew Bruce

Understanding how housing values evolve over time is important to policy makers, consumers and real estate professionals.

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 • 27 Feb 2014 • Drausin F. Wulsin, Emily B. Fox, Brian Litt

A goal of our work is to parse these complex epileptic events into distinct dynamic regimes.

no code implementations • 20 Feb 2014 • Raja Hafiz Affandi, Emily B. Fox, Ryan P. Adams, Ben Taskar

Determinantal point processes (DPPs) are well-suited for modeling repulsion and have proven useful in many applications where diversity is desired.

5 code implementations • 17 Feb 2014 • Tianqi Chen, Emily B. Fox, Carlos Guestrin

Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for defining distant proposals with high acceptance probabilities in a Metropolis-Hastings framework, enabling more efficient exploration of the state space than standard random-walk proposals.

no code implementations • 6 Jan 2014 • François Caron, Emily B. Fox

We show that for certain choices of such exchangeable random measures underlying our graph construction, our network process is sparse with power-law degree distribution.

no code implementations • 12 Nov 2013 • Raja Hafiz Affandi, Emily B. Fox, Ben Taskar

Determinantal point processes (DPPs) are random point processes well-suited for modeling repulsion.

no code implementations • 13 Sep 2013 • Emily B. Fox, Michael. I. Jordan

Although much of the literature on mixed membership models considers the setting in which exchangeable collections of data are associated with each member of a set of entities, it is equally natural to consider problems in which an entire time series is viewed as an entity and the goal is to characterize the time series in terms of a set of underlying dynamic attributes or "dynamic regimes".

no code implementations • 22 Aug 2013 • Emily B. Fox, Michael C. Hughes, Erik B. Sudderth, Michael. I. Jordan

We propose a Bayesian nonparametric approach to the problem of jointly modeling multiple related time series.

1 code implementation • 19 Mar 2010 • Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky

Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes.

no code implementations • 15 May 2009 • Emily B. Fox, Erik B. Sudderth, Michael. I. Jordan, Alan S. Willsky

To address this problem, we take a Bayesian nonparametric approach to speaker diarization that builds on the hierarchical Dirichlet process hidden Markov model (HDP-HMM) of Teh et al. [J. Amer.

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