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20142018
most citedNeural Granger Causality

284 citations · 335 across the 7 of their papers we have counts for

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5 papers · 1 filter

stat.ML2018★ 284 cited

Neural Granger Causality

Alex Tank, Ian Covert, Nicholas Foti +2

While most classical approaches to Granger causality detection assume linear dynamics, many interactions in real-world applications, like neuroscience and genomics, are inherently…

stat.ML2017★ 3 cited

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

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…

stat.ML2017★ 12 cited

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

Alex Tank, Ian Cover, Nicholas J. Foti +2

While most classical approaches to Granger causality detection repose upon linear time series assumptions, many interactions in neuroscience and economics applications are nonlinea…

stat.ML2017★ 8 cited

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

Christopher Xie, Alex Tank, Alec Greaves-Tunnell +1

Providing long-range forecasts is a fundamental challenge in time series modeling, which is only compounded by the challenge of having to form such forecasts when a time series has…

stat.ML2014★ 8 cited

Streaming Variational Inference for Bayesian Nonparametric Mixture Models

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. Unfortunately, suc…