Search Results for author: Nicholas J. Foti

Found 13 papers, 4 papers with code

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.

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.

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 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

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.


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

The Cultural Evolution of National Constitutions

no code implementations18 Nov 2017 Daniel N. Rockmore, Chen Fang, Nicholas J. Foti, Tom Ginsburg, David C. Krakauer

We explore how ideas from infectious disease and genetics can be used to uncover patterns of cultural inheritance and innovation in a corpus of 591 national constitutions spanning 1789 - 2008.

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

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

A survey of non-exchangeable priors for Bayesian nonparametric models

no code implementations20 Nov 2012 Nicholas J. Foti, Sinead Williamson

Dependent nonparametric processes extend distributions over measures, such as the Dirichlet process and the beta process, to give distributions over collections of measures, typically indexed by values in some covariate space.

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