David Hong - RankMe

26 papers Misc 1Journal 19Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Certificate for Orthogonal Equivalence of Real Polynomials by Polynomial-Weighted Principal Component Analysis. CoRR Martin Helmer, David Hong, Hoon Hong
2026	J	`jnl`	Generalized Canonical Polyadic Tensor Decompositions with General Symmetry. CoRR Alex Mulrooney, David Hong
2025	J	`jnl`	Optimal Sample Acquisition for Optimally Weighted PCA From Heterogeneous Quality Sources. IEEE Signal Process. Lett. David Hong, Laura Balzano
2025	J	`jnl`	Streaming Heteroscedastic Probabilistic PCA with Missing Data. Trans. Mach. Learn. Res. Kyle Gilman, David Hong, Jeffrey A. Fessler, Laura Balzano
2023	J	`jnl`	Optimally Weighted PCA for High-Dimensional Heteroscedastic Data. SIAM J. Math. Data Sci. David Hong, Fan Yang, Jeffrey A. Fessler, Laura Balzano
2023	J	`jnl`	Provable Tradeoffs in Adversarially Robust Classification. IEEE Trans. Inf. Theory Edgar Dobriban, Hamed Hassani, David Hong, Alexander Robey
2021	J	`jnl`	HePPCAT: Probabilistic PCA for Data With Heteroscedastic Noise. IEEE Trans. Signal Process. David Hong, Kyle Gilman, Laura Balzano, Jeffrey A. Fessler
2020	J	`jnl`	Baseline Estimation of Commercial Building HVAC Fan Power Using Tensor Completion. CoRR Shunbo Lei, David Hong, Johanna L. Mathieu, Ian A. Hiskens
2020	J	`jnl`	Generalized Canonical Polyadic Tensor Decomposition. SIAM Rev. David Hong, Tamara G. Kolda, Jed A. Duersch
2020	J	`jnl`	Provable tradeoffs in adversarially robust classification. CoRR Edgar Dobriban, Hamed Hassani, David Hong, Alexander Robey
2020	J	`jnl`	Stochastic Gradients for Large-Scale Tensor Decomposition. SIAM J. Math. Data Sci. Tamara G. Kolda, David Hong
2019	J	`jnl`	Convolutional Analysis Operator Learning: Dependence on Training Data. CoRR Il Yong Chun, David Hong, Ben Adcock, Jeffrey A. Fessler
2019	J	`jnl`	Convolutional Analysis Operator Learning: Dependence on Training Data. IEEE Signal Process. Lett. Il Yong Chun, David Hong, Ben Adcock, Jeffrey A. Fessler
2019	—	`conf`	Exploration of tensor decomposition applied to commercial building baseline estimation. GlobalSIP David Hong, Shunbo Lei, Johanna L. Mathieu, Laura Balzano
2019	—	`conf`	Incorporating Handcrafted Filters in Convolutional Analysis Operator Learning for Ill-Posed Inverse Problems. CAMSAP Caroline Crockett, David Hong, Il Yong Chun, Jeffrey A. Fessler
2019	—	`conf`	Probabilistic PCA for Heteroscedastic Data. CAMSAP David Hong, Laura Balzano, Jeffrey A. Fessler
2019	J	`jnl`	Stochastic Gradients for Large-Scale Tensor Decomposition. CoRR Tamara G. Kolda, David Hong
2018	J	`jnl`	Asymptotic performance of PCA for high-dimensional heteroscedastic data. J. Multivar. Anal. David Hong, Laura Balzano, Jeffrey A. Fessler
2018	J	`jnl`	Generalized Canonical Polyadic Tensor Decomposition. CoRR David Hong, Tamara G. Kolda, Jed A. Duersch
2018	—	`conf`	Learning Dictionary-Based Unions of Subspaces for Image Denoising. EUSIPCO David Hong, Robert P. Malinas, Jeffrey A. Fessler, Laura Balzano
2018	J	`jnl`	Multi-Table Differential Correlation Analysis of Neuroanatomical and Cognitive Interactions in Turner Syndrome. Neuroinformatics Christof Seiler, Tamar Green, David Hong, Lindsay Chromik, Lynne Huffman, Susan P. Holmes, Allan L. Reiss
2018	—	`conf`	Online Estimation of Coherent Subspaces with Adaptive Sampling. SSP Greg Ongie, David Hong, Dejiao Zhang, Laura Balzano
2017	Misc	`conf`	Enhanced online subspace estimation via adaptive sensing. ACSSC Greg Ongie, David Hong, Dejiao Zhang, Laura Balzano
2017	J	`jnl`	Instant Accident Reporting and Crowdsensed Road Condition Analytics for Smart Cities. CoRR Ashkan Yousefpour, Caleb Fung, Tam Nguyen, David Hong, Daniel Zhang
2017	J	`jnl`	Subspace Clustering using Ensembles of $K$-Subspaces. CoRR John Lipor, David Hong, Dejiao Zhang, Laura Balzano
2016	—	`conf`	Towards a theoretical analysis of PCA for heteroscedastic data. Allerton David Hong, Laura Balzano, Jeffrey A. Fessler