Publications (18)
Baseline Estimation of Commercial Building HVAC Fan Power Using Tensor Completion
Shunbo Lei, David Hong, Johanna L. Mathieu +1
Commercial building heating, ventilation, and air conditioning (HVAC) systems have been studied for providing ancillary services to power grids via demand response (DR). One critic…
HePPCAT: Probabilistic PCA for Data with Heteroscedastic Noise
David Hong, Kyle Gilman, Laura Balzano +1
Principal component analysis (PCA) is a classical and ubiquitous method for reducing data dimensionality, but it is suboptimal for heterogeneous data that are increasingly common i…
Asymptotic performance of PCA for high-dimensional heteroscedastic data
David Hong, Laura Balzano, Jeffrey A. Fessler
Principal Component Analysis (PCA) is a classical method for reducing the dimensionality of data by projecting them onto a subspace that captures most of their variation. Effective…
Provable tradeoffs in adversarially robust classification
Edgar Dobriban, Hamed Hassani, David Hong +1
It is well known that machine learning methods can be vulnerable to adversarially-chosen perturbations of their inputs. Despite significant progress in the area, foundational open…
Generic Properties of Koopman Eigenfunctions for Stable Fixed Points and Periodic Orbits
Matthew D. Kvalheim, David Hong, Shai Revzen
Our recent work established existence and uniqueness results for globally defined linearizing semiconjugacies for flows having a g…
Convolutional Analysis Operator Learning: Dependence on Training Data
Il Yong Chun, David Hong, Ben Adcock +1
Convolutional analysis operator learning (CAOL) enables the unsupervised training of (hierarchical) convolutional sparsifying operators or autoencoders from large datasets. One can…
Towards a Theoretical Analysis of PCA for Heteroscedastic Data
David Hong, Laura Balzano, Jeffrey A. Fessler
Principal Component Analysis (PCA) is a method for estimating a subspace given noisy samples. It is useful in a variety of problems ranging from dimensionality reduction to anomaly…
Generalized Canonical Polyadic Tensor Decompositions with General Symmetry
Alex Mulrooney, David Hong
Canonical Polyadic (CP) tensor decomposition is a workhorse algorithm for discovering underlying low-dimensional structure in tensor data. This is accomplished in conventional CP d…
Instant Accident Reporting and Crowdsensed Road Condition Analytics for Smart Cities
Ashkan Yousefpour, Caleb Fung, Tam Nguyen +2
The following report contains information about a proposed technology by the authors, which consists of a device that sits inside of a vehicle and constantly monitors the car infor…
Network Signflip Parallel Analysis for Selecting the Embedding Dimension
David Hong, Joshua Cape
The paper proposes a data‑driven spectral technique called NetFlipPA that determines how many dimensions to keep when embedding large heterogeneous networks by comparing eigenvalue…
Generalized Canonical Polyadic Tensor Decomposition
David Hong, Tamara G. Kolda, Jed A. Duersch
Tensor decomposition is a fundamental unsupervised machine learning method in data science, with applications including network analysis and sensor data processing. This work devel…
Beyond Output Correctness: Benchmarking and Evaluating Large Language Model Reasoning in Coding Tasks
Yuangang Li, Justin Tian Jin Chen, Ethan Yu +2
Large language models (LLMs) increasingly rely on explicit reasoning to solve coding tasks, yet evaluating the quality of this reasoning remains challenging. Existing reasoning eva…
Streaming Heteroscedastic Probabilistic PCA with Missing Data
Kyle Gilman, David Hong, Jeffrey A. Fessler +1
Streaming principal component analysis (PCA) is an integral tool in large-scale machine learning for rapidly estimating low-dimensional subspaces from very high-dimensional data ar…
Certificate for Orthogonal Equivalence of Real Polynomials by Polynomial-Weighted Principal Component Analysis
Martin Helmer, David Hong, Hoon Hong
Suppose that and are two real polynomials of degree in variables. If the polynomials and …
Optimally Weighted PCA for High-Dimensional Heteroscedastic Data
David Hong, Fan Yang, Jeffrey A. Fessler +1
Modern data are increasingly both high-dimensional and heteroscedastic. This paper considers the challenge of estimating underlying principal components from high-dimensional data…
Selecting the number of components in PCA via random signflips
David Hong, Yue Sheng, Edgar Dobriban
Principal component analysis (PCA) is a foundational tool in modern data analysis, and a crucial step in PCA is selecting the number of components to keep. However, classical selec…
Subspace Clustering using Ensembles of -Subspaces
John Lipor, David Hong, Yan Shuo Tan +1
Subspace clustering is the unsupervised grouping of points lying near a union of low-dimensional linear subspaces. Algorithms based directly on geometric properties of such data te…
Stochastic Gradients for Large-Scale Tensor Decomposition
Tamara G. Kolda, David Hong
Tensor decomposition is a well-known tool for multiway data analysis. This work proposes using stochastic gradients for efficient generalized canonical polyadic (GCP) tensor decomp…