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An Adaptive Sublinear-Time Block Sparse Fourier Transform

Volkan Cevher, Mikhail Kapralov, Jonathan Mark Scarlett, Amir Zandieh

The problem of approximately computing the

k

dominant Fourier coefficients of a vector

X

quickly, and using few samples in time domain, is known as the Sparse Fourier Transform (sparse FFT) problem. A long line of work on the sparse FFT has resulted in ...

2017

Volkan Cevher

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Finding Mixed Nash Equilibria of Generative Adversarial Networks

Smoothing Alternating Direction Methods for Fully Nonsmooth Constrained Convex Optimization

Convergence of the Exponentiated Gradient Method with Armijo Line Search

Near-Optimal Noisy Group Testing via Separate Decoding of Items

A Single-Phase, Proximal Path-Following Framework

Faster Coordinate Descent via Adaptive Importance Sampling

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach

Smoothing technique for nonsmooth composite minimization with linear operator

General Proximal Gradient Method: A Case for Non-Euclidean Norms

Convergence of the Exponentiated Gradient Method with Armijo Line Search

Smoothing technique for nonsmooth composite minimization with linear operator

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Finding Mixed Nash Equilibria of Generative Adversarial Networks

Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach

Faster Coordinate Descent via Adaptive Importance Sampling

Smoothing Alternating Direction Methods for Fully Nonsmooth Constrained Convex Optimization

General Proximal Gradient Method: A Case for Non-Euclidean Norms

Near-Optimal Noisy Group Testing via Separate Decoding of Items

A Single-Phase, Proximal Path-Following Framework