David S. Matteson

44 papers A* 4A 4C 1Misc 1Journal 31Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Bayesian Changepoint Detection via Logistic Regression and the Topological Analysis of Image Series. Technometrics Andrew M. Thomas, Michael Jauch, David S. Matteson
2025	J	`jnl`	Drift versus Shift: Decoupling Trends and Changepoint Analysis. Technometrics Haoxuan Wu, Toryn L. J. Schafer, Sean E. Ryan, David S. Matteson
2025	J	`jnl`	Dynamic Atomic Column Detection in Transmission Electron Microscopy Videos via Ridge Estimation. IEEE Trans. Image Process. Yuchen Xu, Andrew M. Thomas, Peter A. Crozier, David S. Matteson
2025	J	`jnl`	Dynamic Bayesian Item Response Model with Decomposition (D-BIRD): Modeling Cohort and Individual Learning Over Time. CoRR Hansol Lee, Jason B. Cho, David S. Matteson, Benjamin W. Domingue
2025	J	`jnl`	Likelihood Inference for Possibly Nonstationary Processes via Adaptive Overdifferencing. Technometrics Maryclare Griffin, Gennady Samorodnitsky, David S. Matteson
2025	J	`jnl`	Locally Adaptive Shrinkage Priors for Trends and Breaks in Count Time Series. Technometrics Toryn L. J. Schafer, David S. Matteson
2023	J	`jnl`	Dynamic Atomic Column Detection in Transmission Electron Microscopy Videos via Ridge Estimation. CoRR Yuchen Xu, Andrew M. Thomas, Peter A. Crozier, David S. Matteson
2023	J	`jnl`	Feature Detection and Hypothesis Testing for Extremely Noisy Nanoparticle Images using Topological Data Analysis. Technometrics Andrew M. Thomas, Peter A. Crozier, Yuchen Xu, David S. Matteson
2023	J	`jnl`	Spatial correlation in weather forecast accuracy: a functional time series approach. Comput. Stat. Phillip A. Jang, David S. Matteson
2022	J	`jnl`	Bayesian Spillover Graphs for Dynamic Networks. CoRR Grace Deng, David S. Matteson
2022	A	`conf`	Bayesian spillover graphs for dynamic networks. UAI Grace Deng, David S. Matteson
2022	J	`jnl`	Deep Denoising for Scientific Discovery: A Case Study in Electron Microscopy. IEEE Trans. Computational Imaging Sreyas Mohan, Ramon Manzorro, Joshua L. Vincent, Binh Tang, Dev Yashpal Sheth, Eero P. Simoncelli, David S. Matteson, Peter A. Crozier, Carlos Fernandez-Granda
2022	J	`jnl`	Extended missing data imputation via GANs for ranking applications. Data Min. Knowl. Discov. Grace Deng, Cuize Han, David S. Matteson
2022	J	`jnl`	Factor analysis of mixed data for anomaly detection. Stat. Anal. Data Min. Matthew Davidow, David S. Matteson
2022	J	`jnl`	Graphical Influence Diagnostics for Changepoint Models. J. Comput. Graph. Stat. Ines Wilms, Rebecca Killick, David S. Matteson
2022	J	`jnl`	Group linear non-Gaussian component analysis with applications to neuroimaging. Comput. Stat. Data Anal. Yuxuan Zhao, David S. Matteson, Stewart H. Mostofsky, Mary Beth Nebel, Benjamin B. Risk
2022	A	`conf`	IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance. SDM Grace Deng, Cuize Han, Tommaso Dreossi, Clarence Lee, David S. Matteson
2022	J	`jnl`	Interpretable Latent Variables in Deep State Space Models. CoRR Haoxuan Wu, David S. Matteson, Martin T. Wells
2021	J	`jnl`	AURORA: A Unified fRamework fOR Anomaly detection on multivariate time series. Data Min. Knowl. Discov. Lin Zhang, Wenyu Zhang, Maxwell J. McNeil, Nachuan Chengwang, David S. Matteson, Petko Bogdanov
2021	J	`jnl`	Copula Quadrant Similarity for Anomaly Scores. CoRR Matthew Davidow, David S. Matteson
2021	A*	`conf`	Graph-Based Continual Learning. ICLR Binh Tang, David S. Matteson
2021	J	`jnl`	IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance. CoRR Grace Deng, Cuize Han, Tommaso Dreossi, Clarence Lee, David S. Matteson
2021	A*	`conf`	Probabilistic Transformer For Time Series Analysis. NeurIPS Binh Tang, David S. Matteson
2021	J	`jnl`	PyXtal_FF: a python library for automated force field generation. Mach. Learn. Sci. Technol. Howard Yanxon, David Zagaceta, Binh Tang, David S. Matteson, Qiang Zhu
2021	A*	`conf`	Risk Identification & Quantification in Complex Human-Natural Systems via Convergent Data Intensive Research. KDD 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
2020	J	`jnl`	Deep Denoising For Scientific Discovery: A Case Study In Electron Microscopy. CoRR Sreyas Mohan, Ramon Manzorro, Joshua L. Vincent, Binh Tang, Dev Yashpal Sheth, Eero P. Simoncelli, David S. Matteson, Peter A. Crozier, Carlos Fernandez-Granda
2020	J	`jnl`	Factor Analysis of Mixed Data for Anomaly Detection. CoRR Matthew Davidow, David S. Matteson
2020	J	`jnl`	Graph-Based Continual Learning. CoRR Binh Tang, David S. Matteson
2020	J	`jnl`	High Dimensional Forecasting via Interpretable Vector Autoregression. J. Mach. Learn. Res. William B. Nicholson, Ines Wilms, Jacob Bien, David S. Matteson
2020	J	`jnl`	Learning to Rank with Missing Data via Generative Adversarial Networks. CoRR Grace Deng, Cuize Han, David S. Matteson
2019	A	`conf`	ABACUS: Unsupervised Multivariate Change Detection via Bayesian Source Separation. SDM Wenyu Zhang, Daniel E. Gilbert, David S. Matteson
2019	C	`conf`	Independent Component Analysis Based on Mutual Dependence Measures. ICMLA Ze Jin, David S. Matteson, Tianrong Zhang
2019	J	`jnl`	Optimization and testing in linear non-Gaussian component analysis. Stat. Anal. Data Min. Ze Jin, Benjamin B. Risk, David S. Matteson
2018	J	`jnl`	Band Depth Clustering for Nonstationary Time Series and Wind Speed Behavior. Technometrics Laura L. Tupper, David S. Matteson, C. Lindsay Anderson, Luckny Zéphyr
2018	J	`jnl`	Generalizing distance covariance to measure and test multivariate mutual dependence via complete and incomplete V-statistics. J. Multivar. Anal. Ze Jin, David S. Matteson
2018	A	`conf`	Testing for Conditional Mean Independence with Covariates through Martingale Difference Divergence. UAI Ze Jin, Xiaohan Yan, David S. Matteson
2017	—	`conf`	Pruning and Nonparametric Multiple Change Point Detection. ICDM Workshops Wenyu Zhang, Nicholas A. James, David S. Matteson
2016	J	`jnl`	Large-network travel time distribution estimation for ambulances. Eur. J. Oper. Res. Bradford S. Westgate, Dawn B. Woodard, David S. Matteson, Shane G. Henderson
2016	—	`conf`	Leveraging cloud data to mitigate user experience from 'breaking bad'. IEEE BigData Nicholas A. James, Arun Kejariwal, David S. Matteson
2016	—	`conf`	Mixed data and classification of transit stops. IEEE BigData Laura L. Tupper, David S. Matteson, John C. Handley
2016	J	`jnl`	Spatiotemporal mixed modeling of multi-subject task fMRI via method of moments. NeuroImage Benjamin B. Risk, David S. Matteson, R. Nathan Spreng, David Ruppert
2015	A*	`conf`	Predicting Ambulance Demand: a Spatio-Temporal Kernel Approach. KDD Zhengyi Zhou, David S. Matteson
2013	Misc	`conf`	Locally stationary vector processes and adaptive multivariate modeling. ICASSP David S. Matteson, Nicholas A. James, William B. Nicholson, Louis C. Segalini
2011	J	`jnl`	Time-Series Models of Dynamic Volatility and Correlation. IEEE Signal Process. Mag. David S. Matteson, David Ruppert