133 citations · 803 across the 68 of their papers we have counts for
84 papers
Diagnosing the Refuted Mismatched Decoding Converse for Binary-Input Channels
Jonathan Scarlett
We revisit the claimed converse theorem for mismatched decoding over binary-input discrete memoryless channels (Balakirsky, 1995) and the subsequent work refuting this converse via…
Maxflow-Based Bounds for Low-Rate Information Propagation over Noisy Networks
Yan Hao Ling, Jonathan Scarlett
We study error exponents for the problem of low-rate communication over a directed graph, where each edge in the graph represents a noisy communication channel, and there is a sing…
Communication-Constrained Bandits under Additive Gaussian Noise
Prathamesh Mayekar, Jonathan Scarlett, Vincent Y. F. Tan
We study a distributed stochastic multi-armed bandit where a client supplies the learner with communication-constrained feedback based on the rewards for the corresponding arm pull…
Regret Bounds for Noise-Free Cascaded Kernelized Bandits
Zihan Li, Jonathan Scarlett
We consider optimizing a function network in the noise-free grey-box setting with RKHS function classes, where the exact intermediate results are observable. We assume that the str…
Benefits of Monotonicity in Safe Exploration with Gaussian Processes
Arpan Losalka, Jonathan Scarlett
We consider the problem of sequentially maximising an unknown function over a set of actions while ensuring that every sampled point has a function value below a given safety thres…
Max-Quantile Grouped Infinite-Arm Bandits
Ivan Lau, Yan Hao Ling, Mayank Shrivastava +1
In this paper, we consider a bandit problem in which there are a number of groups each consisting of infinitely many arms. Whenever a new arm is requested from a given group, its m…
Multi-Bit Relaying over a Tandem of Channels
Yan Hao Ling, Jonathan Scarlett
We study error exponents for the problem of relaying a message over a tandem of two channels sharing the same transition law, in particular moving beyond the 1-bit setting studied…
Theoretical Perspectives on Deep Learning Methods in Inverse Problems
Jonathan Scarlett, Reinhard Heckel, Miguel R. D. Rodrigues +2
In recent years, there have been significant advances in the use of deep learning methods in inverse problems such as denoising, compressive sensing, inpainting, and super-resoluti…
Model-Based and Graph-Based Priors for Group Testing
Ivan Lau, Jonathan Scarlett, Yang Sun
The goal of the group testing problem is to identify a set of defective items within a larger set of items, using suitably-designed tests whose outcomes indicate whether any defect…
A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits
Ilija Bogunovic, Zihan Li, Andreas Krause +1
We consider the sequential optimization of an unknown, continuous, and expensive to evaluate reward function, from noisy and adversarially corrupted observed rewards. When the corr…
Mismatched Rate-Distortion Theory: Ensembles, Bounds, and General Alphabets
Millen Kanabar, Jonathan Scarlett
In this paper, we consider the mismatched rate-distortion problem, in which the encoding is done using a codebook, and the encoder chooses the minimum-distortion codeword according…
Generative Principal Component Analysis
Zhaoqiang Liu, Jiulong Liu, Subhroshekhar Ghosh +2
In this paper, we study the problem of principal component analysis with generative modeling assumptions, adopting a general model for the observed matrix that encompasses notable…
On Average-Case Error Bounds for Kernel-Based Bayesian Quadrature
Xu Cai, Chi Thanh Lam, Jonathan Scarlett
In this paper, we study error bounds for {\em Bayesian quadrature} (BQ), with an emphasis on noisy settings, randomized algorithms, and average-case performance measures. We seek t…
Universal 1-Bit Compressive Sensing for Bounded Dynamic Range Signals
Sidhant Bansal, Arnab Bhattacharyya, Anamay Chaturvedi +1
A {\em universal 1-bit compressive sensing (CS)} scheme consists of a measurement matrix such that all signals belonging to a particular class can be approximately recovere…
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning
Sattar Vakili, Jonathan Scarlett, Da-shan Shiu +1
Kernel-based models such as kernel ridge regression and Gaussian processes are ubiquitous in machine learning applications for regression and optimization. It is well known that a…
Performance Bounds for Group Testing With Doubly-Regular Designs
Nelvin Tan, Way Tan, Jonathan Scarlett
In the group testing problem, the goal is to identify a subset of defective items within a larger set of items based on tests whose outcomes indicate whether any defective item is…
Simple Coding Techniques for Many-Hop Relaying
Yan Hao Ling, Jonathan Scarlett
In this paper, we study the problem of relaying a single bit of information across a series of binary symmetric channels, and the associated trade-off between the number of hops $m…
Adversarial Attacks on Gaussian Process Bandits
Eric Han, Jonathan Scarlett
Gaussian processes (GP) are a widely-adopted tool used to sequentially optimize black-box functions, where evaluations are costly and potentially noisy. Recent works on GP bandits…
Gaussian Process Bandit Optimization with Few Batches
Zihan Li, Jonathan Scarlett
In this paper, we consider the problem of black-box optimization using Gaussian Process (GP) bandit optimization with a small number of batches. Assuming the unknown function has a…
Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priors
Zhaoqiang Liu, Subhroshekhar Ghosh, Jun Han +1
In 1-bit compressive sensing, each measurement is quantized to a single bit, namely the sign of a linear function of an unknown vector, and the goal is to accurately recover the ve…
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors
Zhaoqiang Liu, Subhroshekhar Ghosh, Jonathan Scarlett
Compressive phase retrieval is a popular variant of the standard compressive sensing problem in which the measurements only contain magnitude information. In this paper, motivated…
Noisy Adaptive Group Testing via Noisy Binary Search
Bernard Teo, Jonathan 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 possibly-noisy tests, and has numerous practical ap…
Fast Splitting Algorithms for Sparsity-Constrained and Noisy Group Testing
Eric Price, Jonathan Scarlett, Nelvin Tan
In group testing, the goal is to identify a subset of defective items within a larger set of items based on tests whose outcomes indicate whether at least one defective item is pre…
Optimal Rates of Teaching and Learning Under Uncertainty
Yan Hao Ling, Jonathan Scarlett
In this paper, we consider a recently-proposed model of teaching and learning under uncertainty, in which a teacher receives independent observations of a single bit corrupted by b…