David S. Matteson

Publication Activity (10 Years)

Years Active: 2011-2023
Publications (10 Years): 35

Top Topics

Electron Microscopy

Component Analysis

Change Point Detection

Top Venues

Stat. Anal. Data Min.

Publications

Andrew M. Thomas, Peter A. Crozier, Yuchen Xu, David S. Matteson
Feature Detection and Hypothesis Testing for Extremely Noisy Nanoparticle Images using Topological Data Analysis. Technometrics 65 (4) (2023)
Yuchen Xu, Andrew M. Thomas, Peter A. Crozier, David S. Matteson
Dynamic Atomic Column Detection in Transmission Electron Microscopy Videos via Ridge Estimation. CoRR (2023)
Phillip A. Jang, David S. Matteson
Spatial correlation in weather forecast accuracy: a functional time series approach. Comput. Stat. 38 (3) (2023)
Grace Deng, Cuize Han, David S. Matteson
Extended missing data imputation via GANs for ranking applications. Data Min. Knowl. Discov. 36 (4) (2022)
Haoxuan Wu, David S. Matteson, Martin T. Wells
Interpretable Latent Variables in Deep State Space Models. CoRR (2022)
Grace Deng, David S. Matteson
Bayesian Spillover Graphs for Dynamic Networks. CoRR (2022)
Yuxuan Zhao, David S. Matteson, Stewart H. Mostofsky, Mary Beth Nebel, Benjamin B. Risk
Group linear non-Gaussian component analysis with applications to neuroimaging. Comput. Stat. Data Anal. 171 (2022)
Grace Deng, Cuize Han, Tommaso Dreossi, Clarence Lee, David S. Matteson
IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance. SDM (2022)
Matthew Davidow, David S. Matteson
Factor analysis of mixed data for anomaly detection. Stat. Anal. Data Min. 15 (4) (2022)
Sreyas Mohan, Ramon Manzorro, Joshua L. Vincent, Binh Tang, Dev Yashpal Sheth, Eero P. Simoncelli, David S. Matteson, Peter A. Crozier, Carlos Fernandez-Granda
Deep Denoising for Scientific Discovery: A Case Study in Electron Microscopy. IEEE Trans. Computational Imaging 8 (2022)
Ines Wilms, Rebecca Killick, David S. Matteson
Graphical Influence Diagnostics for Changepoint Models. J. Comput. Graph. Stat. 31 (3) (2022)
Grace Deng, David S. Matteson
Bayesian spillover graphs for dynamic networks. UAI (2022)
Binh Tang, David S. Matteson
Probabilistic Transformer For Time Series Analysis. NeurIPS (2021)
Binh Tang, David S. Matteson
Graph-Based Continual Learning. ICLR (2021)
Matthew Davidow, David S. Matteson
Copula Quadrant Similarity for Anomaly Scores. CoRR (2021)
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
Risk Identification & Quantification in Complex Human-Natural Systems via Convergent Data Intensive Research. KDD (2021)
Howard Yanxon, David Zagaceta, Binh Tang, David S. Matteson, Qiang Zhu
PyXtal_FF: a python library for automated force field generation. Mach. Learn. Sci. Technol. 2 (2) (2021)
Lin Zhang, Wenyu Zhang, Maxwell J. McNeil, Nachuan Chengwang, David S. Matteson, Petko Bogdanov
AURORA: A Unified fRamework fOR Anomaly detection on multivariate time series. Data Min. Knowl. Discov. 35 (5) (2021)
Grace Deng, Cuize Han, Tommaso Dreossi, Clarence Lee, David S. Matteson
IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance. CoRR (2021)
Binh Tang, David S. Matteson
Graph-Based Continual Learning. CoRR (2020)
Matthew Davidow, David S. Matteson
Factor Analysis of Mixed Data for Anomaly Detection. CoRR (2020)
William B. Nicholson, Ines Wilms, Jacob Bien, David S. Matteson
High Dimensional Forecasting via Interpretable Vector Autoregression. J. Mach. Learn. Res. 21 (2020)
Sreyas Mohan, Ramon Manzorro, Joshua L. Vincent, Binh Tang, Dev Yashpal Sheth, Eero P. Simoncelli, David S. Matteson, Peter A. Crozier, Carlos Fernandez-Granda
Deep Denoising For Scientific Discovery: A Case Study In Electron Microscopy. CoRR (2020)
Grace Deng, Cuize Han, David S. Matteson
Learning to Rank with Missing Data via Generative Adversarial Networks. CoRR (2020)
Ze Jin, David S. Matteson, Tianrong Zhang
Independent Component Analysis Based on Mutual Dependence Measures. ICMLA (2019)
Ze Jin, Benjamin B. Risk, David S. Matteson
Optimization and testing in linear non-Gaussian component analysis. Stat. Anal. Data Min. 12 (3) (2019)
Wenyu Zhang, Daniel E. Gilbert, David S. Matteson
ABACUS: Unsupervised Multivariate Change Detection via Bayesian Source Separation. SDM (2019)
Ze Jin, Xiaohan Yan, David S. Matteson
Testing for Conditional Mean Independence with Covariates through Martingale Difference Divergence. UAI (2018)
Laura L. Tupper, David S. Matteson, C. Lindsay Anderson, Luckny Zéphyr
Band Depth Clustering for Nonstationary Time Series and Wind Speed Behavior. Technometrics 60 (2) (2018)
Ze Jin, David S. Matteson
Generalizing distance covariance to measure and test multivariate mutual dependence via complete and incomplete V-statistics. J. Multivar. Anal. 168 (2018)
Wenyu Zhang, Nicholas A. James, David S. Matteson
Pruning and Nonparametric Multiple Change Point Detection. ICDM Workshops (2017)
Nicholas A. James, Arun Kejariwal, David S. Matteson
Leveraging cloud data to mitigate user experience from 'breaking bad'. IEEE BigData (2016)
Laura L. Tupper, David S. Matteson, John C. Handley
Mixed data and classification of transit stops. IEEE BigData (2016)
Bradford S. Westgate, Dawn B. Woodard, David S. Matteson, Shane G. Henderson
Large-network travel time distribution estimation for ambulances. Eur. J. Oper. Res. 252 (1) (2016)
Benjamin B. Risk, David S. Matteson, R. Nathan Spreng, David Ruppert
Spatiotemporal mixed modeling of multi-subject task fMRI via method of moments. NeuroImage 142 (2016)
Zhengyi Zhou, David S. Matteson
Predicting Ambulance Demand: a Spatio-Temporal Kernel Approach. KDD (2015)
David S. Matteson, Nicholas A. James, William B. Nicholson, Louis C. Segalini
Locally stationary vector processes and adaptive multivariate modeling. ICASSP (2013)
David S. Matteson, David Ruppert
Time-Series Models of Dynamic Volatility and Correlation. IEEE Signal Process. Mag. 28 (5) (2011)