Publications by Amir Zandieh | EPFL Graph Search

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This thesis focuses on developing efficient algorithmic tools for processing large datasets. In many modern data analysis tasks, the sheer volume of available datasets far outstrips our abilities to process them. This scenario commonly arises in tasks incl ...

Kernel methods are fundamental tools in machine learning that allow detection of non-linear dependencies between data without explicitly constructing feature vectors in high dimensional spaces. A major disadvantage of kernel methods is their poor scalabili ...

Random binning features, introduced in the seminal paper of Rahimi and Recht '07, are an efficient method for approximating a kernel matrix using locality sensitive hashing. Random binning features provide a very simple and efficient way to approximate the ...

Reconstructing continuous signals based on a small number of discrete samples is a fundamental problem across science and engineering. We are often interested in signals with "simple" Fourier structure - e.g., those involving frequencies within a bounded r ...

Learning set functions is a key challenge arising in many domains, ranging from sketching graphs to black-box optimization with discrete parameters. In this paper we consider the problem of efficiently learning set functions that are defined over a ground ...

Amir Zandieh

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Fourier Sampling in Signal Processing and Numerical Linear Algebra

Oblivious Sketching of High-Degree Polynomial Kernels

Scaling up Kernel Ridge Regression via Locality Sensitive Hashing

A Universal Sampling Method for Reconstructing Signals with Simple Fourier Transforms

Efficiently Learning Fourier Sparse Set Functions