133 citations · 803 across the 68 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
Tight Regret Bounds for Noisy Optimization of a Brownian Motion
Zexin Wang, Vincent Y. F. Tan, Jonathan Scarlett
We consider the problem of Bayesian optimization of a one-dimensional Brownian motion in which the adaptively chosen observations are corrupted by Gaussian noise. We show that…
A Characteristic Function Approach to Deep Implicit Generative Modeling
Abdul Fatir Ansari, Jonathan Scarlett, Harold Soh
Implicit Generative Models (IGMs) such as GANs have emerged as effective data-driven models for generating samples, particularly images. In this paper, we formulate the problem of…
High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups
Paul Rolland, Jonathan Scarlett, Ilija Bogunovic +1
Bayesian optimization (BO) is a popular technique for sequential black-box function optimization, with applications including parameter tuning, robotics, environmental monitoring,…