The Digital Futures team are hosting a series of online talks by The University of Manchester's new Turing Fellows for 2021-22.
Presenting at this event are:
Manuel López-Ibáñez, Senior Lecturer in Decision Sciences and Business Analytics.
Manuel's main expertise is on the application of metaheuristics, including local search, evolutionary algorithms and ant colony optimization, to optimization problems, including continuous, combinatorial, and multi-objective problems. His current research is on the experimental analysis and automatic configuration and tuning of stochastic optimization algorithms, in particular, when applied to multi-objective optimization problems.
Title and abstract to follow.
Neil Walton, Reader, Department of Mathematics.
Neil has conducted research visits at Microsoft Research Cambridge, the Basque Centre for Mathematics and the Automatic Control Laboratory ETH Zurich. Neil is co-investigator on the Turing project 'Artificial Intelligence for Transport Planners'. He is an associate editor at the journals Operations Research and Operations Research Letters.
Neil's talk is titled 'Learning and Information in Stochastic Networks and Queues'
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 reinforcement learning have been applied to queueing systems supported by increasing role of information in decision making. We present observations and new results that help rationalize the application of these areas to queueing systems.
We prove that the MaxWeight and BackPressure policies are an application of Blackwell's Approachability Theorem. This connects queueing theoretic results with adversarial learning. We then discuss the requirements of statistical learning for service parameter estimation. As an example, we show how queue size regret can be bounded when applying a perceptron algorithm to classify service. Next, we discuss the role of state information in improved decision making. Here we contrast the roles of epistemic information (information on uncertain parameters) and aleatoric information (information on an uncertain state). Finally we review recent advances in the theory of reinforcement learning and queueing, as well as, provide discussion on current research challenges.
