Search Results for author: Emily B. Fox

Found 30 papers, 9 papers with code

Granger Causality: A Review and Recent Advances

no code implementations5 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.

Time Series

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

no code implementations25 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.

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

no code implementations25 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.

Readmission Prediction

Representing and Denoising Wearable ECG Recordings

no code implementations30 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.


Modeling patterns of smartphone usage and their relationship to cognitive health

no code implementations13 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.

Adaptively Truncating Backpropagation Through Time to Control Gradient Bias

1 code implementation17 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.

Language Modelling

Stochastic Gradient MCMC for Nonlinear State Space Models

2 code implementations29 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.

Time Series

Stochastic Gradient MCMC for State Space Models

1 code implementation22 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.

Bayesian Inference Time Series

Approximate Collapsed Gibbs Clustering with Expectation Propagation

no code implementations19 Jul 2018 Christopher Aicher, Emily B. Fox

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

Time Series Clustering

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

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.

Disentangled VAE Representations for Multi-Aspect and Missing Data

no code implementations24 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.


Large-Scale Stochastic Sampling from the Probability Simplex

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.

Bayesian Inference Topic Models

An Efficient ADMM Algorithm for Structural Break Detection in Multivariate Time Series

no code implementations22 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.

Time Series

An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality Discovery

1 code implementation22 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.

Time Series

sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo

1 code implementation2 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.

Bayesian Inference

Control Variates for Stochastic Gradient MCMC

1 code implementation16 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.

Stochastic Gradient MCMC Methods for Hidden Markov Models

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.

Bayesian Inference

A Complete Recipe for Stochastic Gradient MCMC

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.

Physical Intuition

Achieving a Hyperlocal Housing Price Index: Overcoming Data Sparsity by Bayesian Dynamical Modeling of Multiple Data Streams

no code implementations5 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.

Streaming Variational Inference for Bayesian Nonparametric Mixture Models

no code implementations1 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.

Variational Inference

Stochastic Variational Inference for Hidden Markov Models

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).

Stochastic Optimization Variational Inference

Modeling the Complex Dynamics and Changing Correlations of Epileptic Events

no code implementations27 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.


Learning the Parameters of Determinantal Point Process Kernels

no code implementations20 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.

Point Processes

Stochastic Gradient Hamiltonian Monte Carlo

5 code implementations17 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.

Efficient Exploration

Sparse graphs using exchangeable random measures

no code implementations6 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.

Efficient Exploration graph construction

Approximate Inference in Continuous Determinantal Point Processes

no code implementations12 Nov 2013 Raja Hafiz Affandi, Emily B. Fox, Ben Taskar

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

Point Processes

Mixed Membership Models for Time Series

no code implementations13 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".

Time Series Time Series Analysis

Bayesian Nonparametric Inference of Switching Linear Dynamical Systems

1 code implementation19 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.

A sticky HDP-HMM with application to speaker diarization

no code implementations15 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.

speaker-diarization Speaker Diarization

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