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Jonathan Mark Scarlett

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Related publications (31)

Page 1 of 2

Limits on Support Recovery With Probabilistic Models: An Information-Theoretic Framework

Volkan Cevher, Jonathan Mark Scarlett

The support recovery problem consists of determining a sparse subset of a set of variables that is relevant in generating a set of observations, and arises in a diverse range of settings, such as compressive sensing, subset selection in regression, and gro ...

Institute of Electrical and Electronics Engineers2017

Phase Transitions in the Pooled Data Problem

Volkan Cevher, Jonathan Mark Scarlett

In this paper, we study the {\em pooled data} problem of identifying the labels associated with a large collection of items, based on a sequence of pooled tests revealing the counts of each label within the pool. In the noiseless setting, we identify an ex ...

2017

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Volkan Cevher, Jonathan Mark Scarlett, Mikhail Kapralov, 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

High Dimensional Bayesian Optimization via Additive Models with Overlapping Groups

Volkan Cevher, Paul Thierry Yves Rolland, Jonathan Mark Scarlett, Ilija Bogunovic

Bayesian optimization (BO) is a popular technique for sequential black-box function optimization, with applications including parameter tuning, robotics, environmental monitoring, and more. One of the most important challenges in BO is the development of a ...

2018

Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation

Volkan Cevher, Jonathan Mark Scarlett, Andreas Krause, Ilija Bogunovic

We present a new algorithm, truncated variance reduction (TruVaR), that treats Bayesian optimization (BO) and level-set estimation (LSE) with Gaussian processes in a unified fashion. The algorithm greedily shrinks a sum of truncated variances within a set ...

2016

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Volkan Cevher, Jonathan Mark Scarlett, Ilija Bogunovic

In this paper, we consider the problem of sequentially optimizing a black-box function

f

based on noisy samples and bandit feedback. We assume that

f

is smooth in the sense of having a bounded norm in some reproducing kernel Hilbert space (RKHS), yield ...

2017

Near-Optimal Noisy Group Testing via Separate Decoding of Items

Volkan Cevher, Jonathan Mark Scarlett

The group testing problem consists of determining a small set of defective items from a larger set of items based on a number of tests, and is relevant in applications such as medical testing, communication protocols, pattern matching, and more. In this pa ...

2018

On the Dispersions of the Gel'fand-Pinsker Channel and Dirty Paper Coding

Jonathan Mark Scarlett

This paper studies the second-order coding rates for memoryless channels with a state sequence known non-causally at the encoder. In the case of finite alphabets, an achievability result is obtained using constant-composition random coding, and by using a ...

Institute of Electrical and Electronics Engineers2015

Learning-Based Compressive MRI

Volkan Cevher, Jonathan Mark Scarlett, Yen-Huan Li, Baran Gözcü, Rabeeh Karimi Mahabadi

In the area of magnetic resonance imaging (MRI), an extensive range of non-linear reconstruction algorithms have been proposed that can be used with general Fourier subsampling patterns. However, the design of these subsampling patterns has typically been ...

Institute of Electrical and Electronics Engineers2018

Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices

Volkan Cevher, Jonathan Mark Scarlett

Linear sketching is a powerful tool for the problem of sparse signal recovery, having numerous applications such as compressive sensing, data stream computing, graph sketching, and routing. Motivated by applications where the \emph{positions} of the non-ze ...

2016

Second-order asymptotics for the Gaussian MAC with degraded message sets

Jonathan Mark Scarlett

This paper studies the second-order asymptotics of the Gaussian multiple-access channel with degraded message sets. For a fixed average error probability epsilon is an element of (0, 1) and an arbitrary point on the boundary of the capacity region, we char ...

Institute of Electrical and Electronics Engineers2015

Active Learning of Self-concordant like Multi-index Functions

Volkan Cevher, Jonathan Mark Scarlett, Ilija Bogunovic

We study the problem of actively learning a multi-index function of the form f (x) = g_0 (A_0 x) from its point evaluations, where A_0 ∈ R_{k×d} with k ≪ d. We build on the assumptions and techniques of an existing approach based on low-rank matrix recover ...

2015

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Volkan Cevher, Jonathan Mark Scarlett, Yen-Huan Li

We consider the model selection consistency or sparsistency of a broad set of

\ell_1

-regularized

M

-estimators for linear and non-linear statistical models in a unified fashion. For this purpose, we propose the local structured smoothness condition (LSS ...

2015

Learning-based subsampling

Volkan Cevher, Jonathan Mark Scarlett, Ilija Bogunovic, Yen-Huan Li, Luca Baldassarre, Baran Gözcü

The present invention concerns a method of sampling a test signal. The method comprises: acquiring (21) training signals sampled at a plurality of sampling locations; running (23) an optimization procedure for determining an index set of n indices, represe ...

2017

Second-Order Rate Region of Constant-Composition Codes for the Multiple-Access Channel

Jonathan Mark Scarlett, Albert Guillén i Fàbregas

This paper studies the second-order asymptotics of coding rates for the discrete memoryless multiple-access channel (MAC) with a fixed target error probability. Using constant-composition random coding, coded time-sharing, and a variant of Hoeffding's comb ...

Ieee-Inst Electrical Electronics Engineers Inc2015

On the Difficulty of Selecting Ising Models with Approximate Recovery

Volkan Cevher, Jonathan Mark Scarlett

In this paper, we consider the problem of estimating the underlying graph associated with an Ising model given a number of independent and identically distributed samples. We adopt an approximate recovery criterion that allows for a number of missed edges ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Converse Bounds for Noisy Group Testing with Arbitrary Measurement Matrices

Volkan Cevher, Jonathan Mark Scarlett

We consider the group testing problem, in which one seeks to identify a subset of defective items within a larger set of items based on a number of noisy tests. While matching achievability and converse bounds are known in several cases of interest for i.i ...

Ieee2016

Lower Bounds on Active Learning for Graphical Model Selection

Volkan Cevher, Jonathan Mark Scarlett

We consider the problem of estimating the underlying graph associated with a Markov random field, with the added twist that the decoding algorithm can iteratively choose which subsets of nodes to sample based on the previous samples, resulting in an active ...

2017

Robust Submodular Maximization: A Non-Uniform Partitioning Approach

Volkan Cevher, Jonathan Mark Scarlett, Slobodan Mitrovic, Ilija Bogunovic

We study the problem of maximizing a monotone submodular function subject to a cardinality constraint

k

, with the added twist that a number of items

\tau

from the returned set may be removed. We focus on the worst-case setting considered in Orlin et al ...

2017

An Introductory Guide to Fano's Inequality with Applications in Statistical Estimation

Volkan Cevher, Jonathan Mark Scarlett

Information theory plays an indispensable role in the development of algorithm-independent impossibility results, both for communication problems and for seemingly distinct areas such as statistics and machine learning. While numerous information-theoretic ...

Cambridge University Press2021