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Found 6 papers, 1 papers with code
Improved Learning-augmented Algorithms for k-means and k-medians Clustering
We consider the problem of clustering in the learning-augmented setting, where we are given a data set in $d$-dimensional Euclidean space, and a label for each data point given by an oracle indicating what subsets of points should be clustered together.
Streaming Submodular Maximization with Differential Privacy
In this work, we study the problem of privately maximizing a submodular function in the streaming setting.
Locally Private $k$-Means Clustering with Constant Multiplicative Approximation and Near-Optimal Additive Error
Recent work on this problem in the locally private setting achieves constant multiplicative approximation with additive error $\tilde{O} (n^{1/2 + a} \cdot k \cdot \max \{\sqrt{d}, \sqrt{k} \})$ and proves a lower bound of $\Omega(\sqrt{n})$ on the additive error for any solution with a constant number of rounds.
Differentially private $k$-means clustering via exponential mechanism and max cover
We introduce a new $(\epsilon_p, \delta_p)$-differentially private algorithm for the $k$-means clustering problem.
Differentially Private Decomposable Submodular Maximization
We extend this work by designing differentially private algorithms for both monotone and non-monotone decomposable submodular maximization under general matroid constraints, with competitive utility guarantees.
Learning Gaussian Graphical Models via Multiplicative Weights
Graphical model selection in Markov random fields is a fundamental problem in statistics and machine learning.
