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Found 5 papers, 0 papers with code
Faster Algorithms for Generalized Mean Densest Subgraph Problem
In this paper, we are the first to show that a standard peeling algorithm can still yield $2^{1/p}$-approximation for the case $0<p < 1$.
$k$-Median Clustering via Metric Embedding: Towards Better Initialization with Differential Privacy
When designing clustering algorithms, the choice of initial centers is crucial for the quality of the learned clusters.
Near-Optimal Correlation Clustering with Privacy
Correlation clustering is a central problem in unsupervised learning, with applications spanning community detection, duplicate detection, automated labelling and many more.
k-Median Clustering via Metric Embedding: Towards Better Initialization with Privacy
Our method, named the HST initialization, can also be easily extended to the setting of differential privacy (DP) to generate private initial centers.
Fréchet Distance for Uncertain Curves
We prove that both the upper and lower bound problems are NP-hard for the continuous Fr\'echet distance in several uncertainty models, and that the upper bound problem remains hard for the discrete Fr\'echet distance.
Computational Geometry
