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Found 10 papers, 2 papers with code
To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontier
Specifically, we identify and outline the empirical Pareto frontier through Tradeoff-between-Fairness-and-Accuracy (TAF) Curves; we then develop a metric to quantify this Pareto frontier through the weighted area under the TAF Curve which we term the Fairness-Area-Under-the-Curve (FAUC).
Multivariate Analysis for Multiple Network Data via Semi-Symmetric Tensor PCA
Remarkably, we show that SS-TPCA achieves the same estimation accuracy as classical matrix PCA, with error proportional to the square root of the number of vertices in the network and not the number of edges as might be expected.
Network Clustering for Latent State and Changepoint Detection
In this paper, we propose a convex approach for the task of network clustering.
Sparse Partial Least Squares for Coarse Noisy Graph Alignment
Graph signal processing (GSP) provides a powerful framework for analyzing signals arising in a variety of domains.
Simultaneous Grouping and Denoising via Sparse Convex Wavelet Clustering
In contrast to common practice which denoises then clusters, our method is a unified, convex approach that performs both simultaneously.
Automatic Registration and Clustering of Time Series
Clustering of time series data exhibits a number of challenges not present in other settings, notably the problem of registration (alignment) of observed signals.
Multi-Rank Sparse and Functional PCA: Manifold Optimization and Iterative Deflation Techniques
We first propose an extension of SFPCA which estimates several principal components simultaneously using manifold optimization techniques to enforce orthogonality constraints.
Splitting Methods for Convex Bi-Clustering and Co-Clustering
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
Convex clustering is a promising new approach to the classical problem of clustering, combining strong performance in empirical studies with rigorous theoretical foundations.
Sparse and Functional Principal Components Analysis
We propose a unified approach to regularized PCA which can induce both sparsity and smoothness in both the row and column principal components.
