David S. Matteson

According to our database1, David S. Matteson authored at least 19 papers between 2011 and 2020.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.



In proceedings 
PhD thesis 


On csauthors.net:


High Dimensional Forecasting via Interpretable Vector Autoregression.
J. Mach. Learn. Res., 2020

Learning to Rank with Missing Data via Generative Adversarial Networks.
CoRR, 2020

Deep Denoising For Scientific Discovery: A Case Study In Electron Microscopy.
CoRR, 2020

Graph-Based Continual Learning.
CoRR, 2020

Factor Analysis of Mixed Data for Anomaly Detection.
CoRR, 2020

Optimization and testing in linear non-Gaussian component analysis.
Stat. Anal. Data Min., 2019

ABACUS: Unsupervised Multivariate Change Detection via Bayesian Source Separation.
Proceedings of the 2019 SIAM International Conference on Data Mining, 2019

Independent Component Analysis Based on Mutual Dependence Measures.
Proceedings of the 18th IEEE International Conference On Machine Learning And Applications, 2019

Band Depth Clustering for Nonstationary Time Series and Wind Speed Behavior.
Technometrics, 2018

Generalizing distance covariance to measure and test multivariate mutual dependence via complete and incomplete V-statistics.
J. Multivar. Anal., 2018

Testing for Conditional Mean Independence with Covariates through Martingale Difference Divergence.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Pruning and Nonparametric Multiple Change Point Detection.
Proceedings of the 2017 IEEE International Conference on Data Mining Workshops, 2017

Spatiotemporal mixed modeling of multi-subject task fMRI via method of moments.
NeuroImage, 2016

Large-network travel time distribution estimation for ambulances.
Eur. J. Oper. Res., 2016

Mixed data and classification of transit stops.
Proceedings of the 2016 IEEE International Conference on Big Data, 2016

Leveraging cloud data to mitigate user experience from 'breaking bad'.
Proceedings of the 2016 IEEE International Conference on Big Data, 2016

Predicting Ambulance Demand: a Spatio-Temporal Kernel Approach.
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Locally stationary vector processes and adaptive multivariate modeling.
Proceedings of the IEEE International Conference on Acoustics, 2013

Time-Series Models of Dynamic Volatility and Correlation.
IEEE Signal Process. Mag., 2011