1 code implementation • 3 Feb 2024 • Shangda Yang, Vitaly Zankin, Maximilian Balandat, Stefan Scherer, Kevin Carlberg, Neil Walton, Kody J. H. Law
We leverage multilevel Monte Carlo (MLMC) to improve the performance of multi-step look-ahead Bayesian optimization (BO) methods that involve nested expectations and maximizations.
no code implementations • 15 Aug 2022 • Kody Law, Neil Walton, Shangda Yang
We apply our results to several different Stochastic Approximation algorithms, specifically Projected Stochastic Gradient Descent, Kiefer-Wolfowitz and Stochastic Frank-Wolfe algorithms.
no code implementations • 18 May 2021 • Neil Walton, Kuang Xu
We review the role of information and learning in the stability and optimization of queueing systems.
no code implementations • 21 Apr 2021 • Alvaro Cabrejas-Egea, Raymond Zhang, Neil Walton
Recently, Intelligent Transportation Systems are leveraging the power of increased sensory coverage and computing power to deliver data-intensive solutions achieving higher levels of performance than traditional systems.
no code implementations • 24 Oct 2020 • Neil Walton
We evaluate the ability of temporal difference learning to track the reward function of a policy as it changes over time.
1 code implementation • 18 Aug 2020 • Jack R. McKenzie, Peter A. Appleby, Thomas House, Neil Walton
Cold-start is a notoriously difficult problem which can occur in recommendation systems, and arises when there is insufficient information to draw inferences for users or items.
no code implementations • 20 Jul 2020 • Denis Denisov, Neil Walton
We consider a policy gradient algorithm applied to a finite-arm bandit problem with Bernoulli rewards.
no code implementations • 20 Jul 2020 • Neil Walton
This is a short communication on a Lyapunov function argument for softmax in bandit problems.
no code implementations • 2 Jul 2019 • Yuqing Zhang, Neil Walton
We develop two pricing models: an adaptive Generalized Linear Model (GLM) and an adaptive Gaussian Process (GP) regression model.