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Found 6 papers, 1 papers with code
Graph-based Clustering under Differential Privacy
In this paper, we present the first differentially private clustering method for arbitrary-shaped node clusters in a graph.
On the Needs for Rotations in Hypercubic Quantization Hashing
The aim of this paper is to endow the well-known family of hypercubic quantization hashing methods with theoretical guarantees.
Streaming Binary Sketching based on Subspace Tracking and Diagonal Uniformization
We demonstrate the quality of our binary sketches through experiments on real data for the nearest neighbors search task in the online setting.
Graph sketching-based Space-efficient Data Clustering
In this paper, we address the problem of recovering arbitrary-shaped data clusters from datasets while facing \emph{high space constraints}, as this is for instance the case in many real-world applications when analysis algorithms are directly deployed on resources-limited mobile devices collecting the data.
Structured adaptive and random spinners for fast machine learning computations
We consider an efficient computational framework for speeding up several machine learning algorithms with almost no loss of accuracy.
TripleSpin - a generic compact paradigm for fast machine learning computations
In particular, as a byproduct of the presented techniques and by using relatively new Berry-Esseen-type CLT for random vectors, we give the first theoretical guarantees for one of the most efficient existing LSH algorithms based on the $\textbf{HD}_{3}\textbf{HD}_{2}\textbf{HD}_{1}$ structured matrix ("Practical and Optimal LSH for Angular Distance").
