5 citations · 5 across the 7 of their papers we have counts for
7 papers
Provable Quantization with Randomized Hadamard Transform
Ying Feng, Piotr Indyk, Michael Kapralov +2
Vector quantization via random projection followed by scalar quantization is a fundamental primitive in machine learning, with applications ranging from similarity search to federa…
Spectral Clustering with Side Information
Hendrik Fichtenberger, Michael Kapralov, Ekaterina Kochetkova +3
In the graph clustering problem with a planted solution, the input is a graph on vertices partitioned into clusters, and the task is to infer the clusters from graph struct…
Generalized Flow in Nearly-linear Time on Moderately Dense Graphs
Shunhua Jiang, Michael Kapralov, Lawrence Li +1
In this paper we consider generalized flow problems where there is an -edge -node directed graph and each edge has a loss factor governing whet…
Spectral Clustering Oracles in Sublinear Time
Grzegorz Gluch, Michael Kapralov, Silvio Lattanzi +2
Given a graph that can be partitioned into disjoint expanders with outer conductance upper bounded by , can we efficiently construct a small space data structure th…
Oblivious Sketching of High-Degree Polynomial Kernels
Thomas D. Ahle, Michael Kapralov, Jakob B. T. Knudsen +4
Kernel methods are fundamental tools in machine learning that allow detection of non-linear dependencies between data without explicitly constructing feature vectors in high dimens…
A Universal Sampling Method for Reconstructing Signals with Simple Fourier Transforms
Haim Avron, Michael Kapralov, Cameron Musco +3
Reconstructing continuous signals from a small number of discrete samples is a fundamental problem across science and engineering. In practice, we are often interested in signals w…