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A Parameterized Theory of PAC Learning
But while the nascent theory of parameterized complexity has allowed us to push beyond the P-NP ``dichotomy'' in classical computational complexity and identify the exact boundaries of tractability for numerous problems, there is no analogue in the domain of sample complexity that could push beyond efficient PAC learnability.
How to Find a Good Explanation for Clustering?
(2) For a given set of points, how to find a decision tree with $k$ leaves minimizing the $k$-means/median objective of the resulting explainable clustering?
EPTAS for $k$-means Clustering of Affine Subspaces
We consider a generalization of the fundamental $k$-means clustering for data with incomplete or corrupted entries.
On Coresets for Fair Clustering in Metric and Euclidean Spaces and Their Applications
The new construction allows us to obtain the first coreset for fair clustering in general metric spaces.
Refined Complexity of PCA with Outliers
Principal component analysis (PCA) is one of the most fundamental procedures in exploratory data analysis and is the basic step in applications ranging from quantitative finance and bioinformatics to image analysis and neuroscience.
