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David S. Matteson
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2020 – today
- 2023
- [j16]Phillip A. Jang
, David S. Matteson:
Spatial correlation in weather forecast accuracy: a functional time series approach. Comput. Stat. 38(3): 1215-1229 (2023) - [j15]Andrew M. Thomas
Feature Detection and Hypothesis Testing for Extremely Noisy Nanoparticle Images using Topological Data Analysis. Technometrics 65(4): 590-603 (2023) - [i9]Yuchen Xu, Andrew M. Thomas, Peter A. Crozier, David S. Matteson:
Dynamic Atomic Column Detection in Transmission Electron Microscopy Videos via Ridge Estimation. CoRR abs/2302.00816 (2023) - 2022
- [j14]Yuxuan Zhao
Group linear non-Gaussian component analysis with applications to neuroimaging. Comput. Stat. Data Anal. 171: 107454 (2022) - [j13]Grace Deng
Extended missing data imputation via GANs for ranking applications. Data Min. Knowl. Discov. 36(4): 1498-1520 (2022) - [j12]Ines Wilms
Graphical Influence Diagnostics for Changepoint Models. J. Comput. Graph. Stat. 31(3): 753-765 (2022) - [j11]Matthew Davidow
Factor analysis of mixed data for anomaly detection. Stat. Anal. Data Min. 15(4): 480-493 (2022) - [j10]Sreyas Mohan
Deep Denoising for Scientific Discovery: A Case Study in Electron Microscopy. IEEE Trans. Computational Imaging 8: 585-597 (2022) - [c13]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: 217-225 - [c12]Grace Deng, David S. Matteson:
Bayesian spillover graphs for dynamic networks. UAI 2022: 529-538 - [i8]Grace Deng, David S. Matteson
Bayesian Spillover Graphs for Dynamic Networks. CoRR abs/2203.01912 (2022) - [i7]Haoxuan Wu, David S. Matteson
Interpretable Latent Variables in Deep State Space Models. CoRR abs/2203.02057 (2022) - 2021
- [j9]Lin Zhang, Wenyu Zhang, Maxwell J. McNeil, Nachuan Chengwang, David S. Matteson
AURORA: A Unified fRamework fOR Anomaly detection on multivariate time series. Data Min. Knowl. Discov. 35(5): 1882-1905 (2021) - [j8]Howard Yanxon
PyXtal_FF: a python library for automated force field generation. Mach. Learn. Sci. Technol. 2(2): 27001 (2021) - [c11]Binh Tang, David S. Matteson:
Graph-Based Continual Learning. ICLR 2021 - [c10]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: 4155-4156 - [c9]Binh Tang, David S. Matteson:
Probabilistic Transformer For Time Series Analysis. NeurIPS 2021: 23592-23608 - [i6]Matthew Davidow, David S. Matteson:
Copula Quadrant Similarity for Anomaly Scores. CoRR abs/2101.02330 (2021) - [i5]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 abs/2110.07460 (2021) - 2020
- [j7]William B. Nicholson, Ines Wilms, Jacob Bien, David S. Matteson:
High Dimensional Forecasting via Interpretable Vector Autoregression. J. Mach. Learn. Res. 21: 166:1-166:52 (2020) - [i4]Matthew Davidow, David S. Matteson:
Factor Analysis of Mixed Data for Anomaly Detection. CoRR abs/2005.12129 (2020) - [i3]Binh Tang, David S. Matteson:
Graph-Based Continual Learning. CoRR abs/2007.04813 (2020) - [i2]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 abs/2010.12970 (2020) - [i1]Grace Deng, Cuize Han, David S. Matteson:
Learning to Rank with Missing Data via Generative Adversarial Networks. CoRR abs/2011.02089 (2020)
2010 – 2019
- 2019
- [j6]Ze Jin
Optimization and testing in linear non-Gaussian component analysis. Stat. Anal. Data Min. 12(3): 141-156 (2019) - [c8]Ze Jin, David S. Matteson
Independent Component Analysis Based on Mutual Dependence Measures. ICMLA 2019: 573-580 - [c7]Wenyu Zhang, Daniel E. Gilbert, David S. Matteson
ABACUS: Unsupervised Multivariate Change Detection via Bayesian Source Separation. SDM 2019: 603-611 - 2018
- [j5]Ze Jin
Generalizing distance covariance to measure and test multivariate mutual dependence via complete and incomplete V-statistics. J. Multivar. Anal. 168: 304-322 (2018) - [j4]Laura L. Tupper
Band Depth Clustering for Nonstationary Time Series and Wind Speed Behavior. Technometrics 60(2): 245-254 (2018) - [c6]Ze Jin, Xiaohan Yan, David S. Matteson:
Testing for Conditional Mean Independence with Covariates through Martingale Difference Divergence. UAI 2018: 1-12 - 2017
- [c5]Wenyu Zhang, Nicholas A. James, David S. Matteson
Pruning and Nonparametric Multiple Change Point Detection. ICDM Workshops 2017: 288-295 - 2016
- [j3]Bradford S. Westgate, Dawn B. Woodard, David S. Matteson
Large-network travel time distribution estimation for ambulances. Eur. J. Oper. Res. 252(1): 322-333 (2016) - [j2]Benjamin B. Risk
Spatiotemporal mixed modeling of multi-subject task fMRI via method of moments. NeuroImage 142: 280-292 (2016) - [c4]Laura L. Tupper, David S. Matteson
Mixed data and classification of transit stops. IEEE BigData 2016: 2225-2232 - [c3]Nicholas A. James, Arun Kejariwal, David S. Matteson
Leveraging cloud data to mitigate user experience from 'breaking bad'. IEEE BigData 2016: 3499-3508 - 2015
- [c2]Zhengyi Zhou
Predicting Ambulance Demand: a Spatio-Temporal Kernel Approach. KDD 2015: 2297-2303 - 2013
- [c1]David S. Matteson
Locally stationary vector processes and adaptive multivariate modeling. ICASSP 2013: 8722-8726 - 2011
- [j1]David S. Matteson
Time-Series Models of Dynamic Volatility and Correlation. IEEE Signal Process. Mag. 28(5): 72-82 (2011)
Coauthor Index
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