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- Moritz Hardt, Eric Price, Nathan Srebro
- NIPS
- 2016

We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features. Assuming data about the… (More)

- Haitham Hassanieh, Piotr Indyk, Dina Katabi, Eric Price
- SODA
- 2012

We consider the sparse Fourier transform problem: given a complex vector x of length n, and a parameter k, estimate the k largest (in magnitude) coefficients of the Fourier transform of x. The… (More)

- Ashish Bora, Ajil Jalal, Eric Price, Alexandros G. Dimakis
- ICML
- 2017

The goal of compressed sensing is to estimate a vector from an underdetermined system of noisy linear measurements, by making use of prior knowledge on the structure of vectors in the relevant… (More)

- Haitham Hassanieh, Piotr Indyk, Dina Katabi, Eric Price
- STOC
- 2012

We consider the problem of computing the k-sparse approximation to the discrete Fourier transform of an n-dimensional signal. We show: An O(k log n)-time randomized algorithm for the case where the… (More)

- Moritz Hardt, Eric Price
- NIPS
- 2013

We provide a new robust convergence analysis of the well-known power method for computing the dominant singular vectors of a matrix that we call the noisy power method. Our result characterizes the… (More)

- Badih Ghazi, Haitham Hassanieh, Piotr Indyk, Dina Katabi, Eric Price, Lixin Shi
- 51st Annual Allerton Conference on Communication…
- 2013

We present the first sample-optimal sublinear time algorithms for the sparse Discrete Fourier Transform over a two-dimensional √n × √n grid. Our algorithms are analyzed for the average case signals.… (More)

- Khanh Do Ba, Piotr Indyk, Eric Price, David P. Woodruff
- SODA
- 2010

We consider the following <i>k</i>-sparse recovery problem: design an <i>m</i> x <i>n</i> matrix <i>A</i>, such that for any signal <i>x</i>, given <i>Ax</i> we can efficiently recover x satisfying… (More)

- Ashish Bora, Eric Price, Alexandros G. Dimakis
- ICLR
- 2018

Generative models provide a way to model structure in complex distributions and have been shown to be useful for many tasks of practical interest. However, current techniques for training generative… (More)

- Xinyang Yi, Constantine Caramanis, Eric Price
- ICML
- 2015

Binary embedding is a nonlinear dimension reduction methodology where high dimensional data are embedded into the Hamming cube while preserving the structure of the original space. Specifically, for… (More)

- Piotr Indyk, Eric Price
- STOC
- 2011

We initiate the study of sparse recovery problems under the Earth-Mover Distance (EMD). Specifically, we design a distribution over m x n matrices A such that for any x, given Ax, we can recover a… (More)