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2020 – today
- 2023
[j8]David Hong
, Fan Yang, Jeffrey A. Fessler, Laura Balzano:
Optimally Weighted PCA for High-Dimensional Heteroscedastic Data. SIAM J. Math. Data Sci. 5(1): 222-250 (2023)- [j7]Edgar Dobriban, Hamed Hassani, David Hong, Alexander Robey:
Provable Tradeoffs in Adversarially Robust Classification. IEEE Trans. Inf. Theory 69(12): 7793-7822 (2023) - 2021
- [j6]David Hong, Kyle Gilman, Laura Balzano, Jeffrey A. Fessler:
HePPCAT: Probabilistic PCA for Data With Heteroscedastic Noise. IEEE Trans. Signal Process. 69: 4819-4834 (2021) - 2020
- [j5]David Hong, Tamara G. Kolda, Jed A. Duersch:
Generalized Canonical Polyadic Tensor Decomposition. SIAM Rev. 62(1): 133-163 (2020) - [j4]Tamara G. Kolda, David Hong:
Stochastic Gradients for Large-Scale Tensor Decomposition. SIAM J. Math. Data Sci. 2(4): 1066-1095 (2020) - [i7]Shunbo Lei, David Hong, Johanna L. Mathieu, Ian A. Hiskens:
Baseline Estimation of Commercial Building HVAC Fan Power Using Tensor Completion. CoRR abs/2004.13557 (2020) - [i6]Edgar Dobriban, Hamed Hassani, David Hong, Alexander Robey:
Provable tradeoffs in adversarially robust classification. CoRR abs/2006.05161 (2020)
2010 – 2019
- 2019
- [j3]Il Yong Chun, David Hong, Ben Adcock, Jeffrey A. Fessler:
Convolutional Analysis Operator Learning: Dependence on Training Data. IEEE Signal Process. Lett. 26(8): 1137-1141 (2019) - [c7]David Hong, Laura Balzano, Jeffrey A. Fessler:
Probabilistic PCA for Heteroscedastic Data. CAMSAP 2019: 26-30 - [c6]Caroline Crockett, David Hong, Il Yong Chun, Jeffrey A. Fessler:
Incorporating Handcrafted Filters in Convolutional Analysis Operator Learning for Ill-Posed Inverse Problems. CAMSAP 2019: 316-320 - [c5]David Hong, Shunbo Lei, Johanna L. Mathieu, Laura Balzano:
Exploration of tensor decomposition applied to commercial building baseline estimation. GlobalSIP 2019: 1-5 - [i5]Il Yong Chun, David Hong, Ben Adcock, Jeffrey A. Fessler:
Convolutional Analysis Operator Learning: Dependence on Training Data. CoRR abs/1902.08267 (2019) - [i4]Tamara G. Kolda, David Hong:
Stochastic Gradients for Large-Scale Tensor Decomposition. CoRR abs/1906.01687 (2019) - 2018
- [j2]David Hong, Laura Balzano, Jeffrey A. Fessler:
Asymptotic performance of PCA for high-dimensional heteroscedastic data. J. Multivar. Anal. 167: 435-452 (2018) - [j1]Christof Seiler, Tamar Green, David Hong, Lindsay Chromik, Lynne Huffman, Susan P. Holmes, Allan L. Reiss:
Multi-Table Differential Correlation Analysis of Neuroanatomical and Cognitive Interactions in Turner Syndrome. Neuroinformatics 16(1): 81-93 (2018) - [c4]David Hong, Robert P. Malinas, Jeffrey A. Fessler, Laura Balzano:
Learning Dictionary-Based Unions of Subspaces for Image Denoising. EUSIPCO 2018: 1597-1601 - [c3]Greg Ongie, David Hong, Dejiao Zhang, Laura Balzano:
Online Estimation of Coherent Subspaces with Adaptive Sampling. SSP 2018: 841-845 - [i3]David Hong, Tamara G. Kolda, Jed A. Duersch:
Generalized Canonical Polyadic Tensor Decomposition. CoRR abs/1808.07452 (2018) - 2017
- [c2]Greg Ongie, David Hong, Dejiao Zhang, Laura Balzano:
Enhanced online subspace estimation via adaptive sensing. ACSSC 2017: 993-997 - [i2]John Lipor, David Hong, Dejiao Zhang, Laura Balzano:
Subspace Clustering using Ensembles of $K$-Subspaces. CoRR abs/1709.04744 (2017) - [i1]Ashkan Yousefpour, Caleb Fung, Tam Nguyen, David Hong, Daniel Zhang:
Instant Accident Reporting and Crowdsensed Road Condition Analytics for Smart Cities. CoRR abs/1711.06710 (2017) - 2016
- [c1]David Hong, Laura Balzano, Jeffrey A. Fessler:
Towards a theoretical analysis of PCA for heteroscedastic data. Allerton 2016: 496-503
Coauthor Index
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