Publications by Jonathan Mark Scarlett

A Distributed Algorithm for Partitioned Robust Submodular Maximization

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

Abstract—In this paper, we consider the problem of maximizing a monotone submodular function subject to a cardinality constraint, with two added twists: The computation is distributed across a number of machines, and we require the solution to be robust ag ...

2017

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

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

How little does non-exact recovery help in group testing?

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 tests. We characterize the information-theoretic performance limits in the presence of list decoding, in whi ...

Ieee2017

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

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

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

Time-Varying Gaussian Process Bandit Optimization

Volkan Cevher, Jonathan Mark Scarlett, Ilija Bogunovic

We consider the sequential Bayesian optimization problem with bandit feedback, adopting a formulation that allows for the reward function to vary with time. We model the reward function using a Gaussian process whose evolution obeys a simple Markov model. ...

2016

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

Volkan Cevher, Andreas Krause, Jonathan Mark Scarlett, 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

Multiuser Random Coding Techniques for Mismatched Decoding

Jonathan Mark Scarlett, Albert Guillén i Fàbregas

This paper studies multiuser random coding techniques for channel coding with a given (possibly suboptimal) decoding rule. For the mismatched discrete memoryless multiple-access channel, an error exponent is obtained that is tight with respect to the ensem ...

Institute of Electrical and Electronics Engineers2016

Jonathan Mark Scarlett

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A Distributed Algorithm for Partitioned Robust Submodular Maximization

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

Lower Bounds on Active Learning for Graphical Model Selection

How little does non-exact recovery help in group testing?

Learning-based subsampling

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Phase Transitions in the Pooled Data Problem

Time-Varying Gaussian Process Bandit Optimization

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

Multiuser Random Coding Techniques for Mismatched Decoding

Phase Transitions in the Pooled Data Problem

Lower Bounds on Active Learning for Graphical Model Selection

Time-Varying Gaussian Process Bandit Optimization

How little does non-exact recovery help in group testing?

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Multiuser Random Coding Techniques for Mismatched Decoding

Learning-based subsampling

A Distributed Algorithm for Partitioned Robust Submodular Maximization

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

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