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Found 4 papers, 2 papers with code
Near-Optimal Quantum Coreset Construction Algorithms for Clustering
$k$-Clustering in $\mathbb{R}^d$ (e. g., $k$-median and $k$-means) is a fundamental machine learning problem.
On The Relative Error of Random Fourier Features for Preserving Kernel Distance
The method of random Fourier features (RFF), proposed in a seminal paper by Rahimi and Recht (NIPS'07), is a powerful technique to find approximate low-dimensional representations of points in (high-dimensional) kernel space, for shift-invariant kernels.
Coresets for Clustering with Fairness Constraints
Our approach is based on novel constructions of coresets: for the $k$-median objective, we construct an $\varepsilon$-coreset of size $O(\Gamma k^2 \varepsilon^{-d})$ where $\Gamma$ is the number of distinct collections of groups that a point may belong to, and for the $k$-means objective, we show how to construct an $\varepsilon$-coreset of size $O(\Gamma k^3\varepsilon^{-d-1})$.
Coresets for Ordered Weighted Clustering
We design coresets for Ordered k-Median, a generalization of classical clustering problems such as k-Median and k-Center, that offers a more flexible data analysis, like easily combining multiple objectives (e. g., to increase fairness or for Pareto optimization).
Data Structures and Algorithms
