29 citations · 77 across the 12 of their papers we have counts for
12 papers
Optimal accuracy for linear sets of equations with the graph Laplacian
Richard B. Lehoucq, Michael Weylandt, Jonathan W. Berry
We show that certain Graph Laplacian linear sets of equations exhibit optimal accuracy, guaranteeing that the relative error is no larger than the norm of the relative residual and…
To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontier
Camille Olivia Little, Michael Weylandt, Genevera I Allen
Algorithmic fairness has emerged as an important consideration when using machine learning to make high-stakes societal decisions. Yet, improved fairness often comes at the expense…
Multivariate Analysis for Multiple Network Data via Semi-Symmetric Tensor PCA
Michael Weylandt, George Michailidis
Network data are commonly collected in a variety of applications, representing either directly measured or statistically inferred connections between features of interest. In an in…
Network Clustering for Latent State and Changepoint Detection
Madeline Navarro, Genevera I. Allen, Michael Weylandt
Network models provide a powerful and flexible framework for analyzing a wide range of structured data sources. In many situations of interest, however, multiple networks can be co…
Sparse Partial Least Squares for Coarse Noisy Graph Alignment
Michael Weylandt, George Michailidis, T. Mitchell Roddenberry
Graph signal processing (GSP) provides a powerful framework for analyzing signals arising in a variety of domains. In many applications of GSP, multiple network structures are avai…
Simultaneous Grouping and Denoising via Sparse Convex Wavelet Clustering
Michael Weylandt, T. Mitchell Roddenberry, Genevera I. Allen
Clustering is a ubiquitous problem in data science and signal processing. In many applications where we observe noisy signals, it is common practice to first denoise the data, perh…
Automatic Registration and Clustering of Time Series
Michael Weylandt, George Michailidis
Clustering of time series data exhibits a number of challenges not present in other settings, notably the problem of registration (alignment) of observed signals. Typical approache…
Multivariate Modeling of Natural Gas Spot Trading Hubs Incorporating Futures Market Realized Volatility
Michael Weylandt, Yu Han, Katherine B. Ensor
Financial markets for Liquified Natural Gas (LNG) are an important and rapidly-growing segment of commodities markets. Like other commodities markets, there is an inherent spatial…
Multi-Rank Sparse and Functional PCA: Manifold Optimization and Iterative Deflation Techniques
Michael Weylandt
We consider the problem of estimating multiple principal components using the recently-proposed Sparse and Functional Principal Components Analysis (SFPCA) estimator. We first prop…
Splitting Methods for Convex Bi-Clustering and Co-Clustering
Michael Weylandt
Co-Clustering, the problem of simultaneously identifying clusters across multiple aspects of a data set, is a natural generalization of clustering to higher-order structured data.…
Dynamic Visualization and Fast Computation for Convex Clustering via Algorithmic Regularization
Michael Weylandt, John Nagorski, Genevera I. Allen
Convex clustering is a promising new approach to the classical problem of clustering, combining strong performance in empirical studies with rigorous theoretical foundations. Despi…
Sparse and Functional Principal Components Analysis
Genevera I. Allen, Michael Weylandt
Regularized variants of Principal Components Analysis, especially Sparse PCA and Functional PCA, are among the most useful tools for the analysis of complex high-dimensional data.…