1 citations · 2 across the 5 of their papers we have counts for
11 papers
Learning and Information in Stochastic Networks and Queues
Neil Walton, Kuang Xu
We review the role of information and learning in the stability and optimization of queueing systems. In recent years, techniques from supervised learning, bandit learning and rein…
Reinforcement Learning for Traffic Signal Control: Comparison with Commercial Systems
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…
An Adiabatic Theorem for Policy Tracking with TD-learning
Neil Walton
We evaluate the ability of temporal difference learning to track the reward function of a policy as it changes over time. Our results apply a new adiabatic theorem that bounds the…
Perturbed Pricing
Neil Walton, Yuqing Zhang
We propose a simple randomized rule for the optimization of prices in revenue management with contextual information. It is known that the certainty equivalent pricing rule, albeit…
Fast Approximate Bayesian Contextual Cold Start Learning (FAB-COST)
Jack R. McKenzie, Peter A. Appleby, Thomas House +1
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. To…
A Short Note on Soft-max and Policy Gradients in Bandits Problems
Neil Walton
This is a short communication on a Lyapunov function argument for softmax in bandit problems. There are a number of excellent papers coming out using differential equations for pol…