David S. Matteson

According to our database, David S. Matteson authored at least 33 papers between 2011 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Feature Detection and Hypothesis Testing for Extremely Noisy Nanoparticle Images using Topological Data Analysis.

Andrew M. Thomas, Peter A. Crozier, Yuchen Xu, David S. Matteson

Technometrics, October, 2023

Spatial correlation in weather forecast accuracy: a functional time series approach.

Phillip A. Jang, David S. Matteson

Comput. Stat., September, 2023

Dynamic Atomic Column Detection in Transmission Electron Microscopy Videos via Ridge Estimation.

Yuchen Xu, Andrew M. Thomas, Peter A. Crozier, David S. Matteson

CoRR, 2023

2022

Graphical Influence Diagnostics for Changepoint Models.

Ines Wilms, Rebecca Killick, David S. Matteson

J. Comput. Graph. Stat., July, 2022

Deep Denoising for Scientific Discovery: A Case Study in Electron Microscopy.

Sreyas Mohan, Ramon Manzorro, Joshua L. Vincent, Binh Tang, Dev Yashpal Sheth, Eero P. Simoncelli, David S. Matteson, Peter A. Crozier, Carlos Fernandez-Granda

IEEE Trans. Computational Imaging, 2022

Factor analysis of mixed data for anomaly detection.

Matthew Davidow, David S. Matteson

Stat. Anal. Data Min., 2022

Extended missing data imputation via GANs for ranking applications.

Grace Deng, Cuize Han, David S. Matteson

Data Min. Knowl. Discov., 2022

Group linear non-Gaussian component analysis with applications to neuroimaging.

Yuxuan Zhao, David S. Matteson, Stewart H. Mostofsky, Mary Beth Nebel, Benjamin B. Risk

Comput. Stat. Data Anal., 2022

Interpretable Latent Variables in Deep State Space Models.

Haoxuan Wu, David S. Matteson, Martin T. Wells

CoRR, 2022

Bayesian spillover graphs for dynamic networks.

Grace Deng, David S. Matteson

Proceedings of the Uncertainty in Artificial Intelligence, 2022

IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance.

Grace Deng, Cuize Han, Tommaso Dreossi, Clarence Lee, David S. Matteson

Proceedings of the 2022 SIAM International Conference on Data Mining, 2022

2021

PyXtal_FF: a python library for automated force field generation.

Howard Yanxon, David Zagaceta, Binh Tang, David S. Matteson, Qiang Zhu

Mach. Learn. Sci. Technol., 2021

AURORA: A Unified fRamework fOR Anomaly detection on multivariate time series.

Lin Zhang, Wenyu Zhang, Maxwell J. McNeil, Nachuan Chengwang, David S. Matteson, Petko Bogdanov

Data Min. Knowl. Discov., 2021

Copula Quadrant Similarity for Anomaly Scores.

Matthew Davidow, David S. Matteson

CoRR, 2021

Probabilistic Transformer For Time Series Analysis.

Binh Tang, David S. Matteson

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Risk Identification & Quantification in Complex Human-Natural Systems via Convergent Data Intensive Research.

Toryn L. J. Schafer, Ryan M. McGranaghan, Mila Getmansky Sherman, Mei-Ling E. Feng, Olukunle O. Owolabi, Sean E. Ryan, Marie-Christine Düker, Michael Jauch, David S. Matteson

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Graph-Based Continual Learning.

Binh Tang, David S. Matteson

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

High Dimensional Forecasting via Interpretable Vector Autoregression.

William B. Nicholson, Ines Wilms, Jacob Bien, David S. Matteson

J. Mach. Learn. Res., 2020

Learning to Rank with Missing Data via Generative Adversarial Networks.

Grace Deng, Cuize Han, David S. Matteson

CoRR, 2020

2019

Optimization and testing in linear non-Gaussian component analysis.

Ze Jin, Benjamin B. Risk, David S. Matteson

Stat. Anal. Data Min., 2019

ABACUS: Unsupervised Multivariate Change Detection via Bayesian Source Separation.

Wenyu Zhang, Daniel E. Gilbert, David S. Matteson

Proceedings of the 2019 SIAM International Conference on Data Mining, 2019

Independent Component Analysis Based on Mutual Dependence Measures.

Ze Jin, David S. Matteson, Tianrong Zhang

Proceedings of the 18th IEEE International Conference On Machine Learning And Applications, 2019

2018

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

Laura L. Tupper, David S. Matteson, C. Lindsay Anderson, Luckny Zéphyr

Technometrics, 2018

Generalizing distance covariance to measure and test multivariate mutual dependence via complete and incomplete V-statistics.

Ze Jin, David S. Matteson

J. Multivar. Anal., 2018

Testing for Conditional Mean Independence with Covariates through Martingale Difference Divergence.

Ze Jin, Xiaohan Yan, David S. Matteson

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

2017

Pruning and Nonparametric Multiple Change Point Detection.

Wenyu Zhang, Nicholas A. James, David S. Matteson

Proceedings of the 2017 IEEE International Conference on Data Mining Workshops, 2017

2016

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

Benjamin B. Risk, David S. Matteson, R. Nathan Spreng, David Ruppert

NeuroImage, 2016

Large-network travel time distribution estimation for ambulances.

Bradford S. Westgate, Dawn B. Woodard, David S. Matteson, Shane G. Henderson

Eur. J. Oper. Res., 2016

Mixed data and classification of transit stops.

Laura L. Tupper, David S. Matteson, John C. Handley

Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData 2016), 2016

Leveraging cloud data to mitigate user experience from 'breaking bad'.

Nicholas A. James, Arun Kejariwal, David S. Matteson

Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData 2016), 2016

2015

Predicting Ambulance Demand: a Spatio-Temporal Kernel Approach.

Zhengyi Zhou, David S. Matteson

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

2013

Locally stationary vector processes and adaptive multivariate modeling.

David S. Matteson, Nicholas A. James, William B. Nicholson, Louis C. Segalini

Proceedings of the IEEE International Conference on Acoustics, 2013

2011

Time-Series Models of Dynamic Volatility and Correlation.

David S. Matteson, David Ruppert

IEEE Signal Process. Mag., 2011