8 research outputs found
Sparse recovery and Fourier sampling
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 155-160).In the last decade a broad literature has arisen studying sparse recovery, the estimation of sparse vectors from low dimensional linear projections. Sparse recovery has a wide variety of applications such as streaming algorithms, image acquisition, and disease testing. A particularly important subclass of sparse recovery is the sparse Fourier transform, which considers the computation of a discrete Fourier transform when the output is sparse. Applications of the sparse Fourier transform include medical imaging, spectrum sensing, and purely computation tasks involving convolution. This thesis describes a coherent set of techniques that achieve optimal or near-optimal upper and lower bounds for a variety of sparse recovery problems. We give the following state-of-the-art algorithms for recovery of an approximately k-sparse vector in n dimensions: -- Two sparse Fourier transform algorithms, respectively taking ... time and ... samples. The latter is within log e log n of the optimal sample complexity when ... -- An algorithm for adaptive sparse recovery using ... measurements, showing that adaptivity can give substantial improvements when k is small. -- An algorithm for C-approximate sparse recovery with ... measurements, which matches our lower bound up to the log* k factor and gives the first improvement for ... In the second part of this thesis, we give lower bounds for the above problems and more.by Eric Price.Ph. D
Federated Empirical Risk Minimization via Second-Order Method
Many convex optimization problems with important applications in machine
learning are formulated as empirical risk minimization (ERM). There are several
examples: linear and logistic regression, LASSO, kernel regression, quantile
regression, -norm regression, support vector machines (SVM), and mean-field
variational inference. To improve data privacy, federated learning is proposed
in machine learning as a framework for training deep learning models on the
network edge without sharing data between participating nodes. In this work, we
present an interior point method (IPM) to solve a general ERM problem under the
federated learning setting. We show that the communication complexity of each
iteration of our IPM is , where is the dimension (i.e.,
number of features) of the dataset
InstaHide: Instance-hiding Schemes for Private Distributed Learning
How can multiple distributed entities collaboratively train a shared deep net
on their private data while preserving privacy? This paper introduces
InstaHide, a simple encryption of training images, which can be plugged into
existing distributed deep learning pipelines. The encryption is efficient and
applying it during training has minor effect on test accuracy.
InstaHide encrypts each training image with a "one-time secret key" which
consists of mixing a number of randomly chosen images and applying a random
pixel-wise mask. Other contributions of this paper include: (a) Using a large
public dataset (e.g. ImageNet) for mixing during its encryption, which improves
security. (b) Experimental results to show effectiveness in preserving privacy
against known attacks with only minor effects on accuracy. (c) Theoretical
analysis showing that successfully attacking privacy requires attackers to
solve a difficult computational problem. (d) Demonstrating that use of the
pixel-wise mask is important for security, since Mixup alone is shown to be
insecure to some some efficient attacks. (e) Release of a challenge dataset
https://github.com/Hazelsuko07/InstaHide_Challenge
Our code is available at https://github.com/Hazelsuko07/InstaHideComment: ICML 202