Professor David Leslie

Professor of Statistics


I am a Professor of Statustucal Learning in the Department of Mathematics and Statistics at Lancaster University. I research statistical learning, decision-making, and game theory. My research on bandit algorithms is used by many of the world's largest companies to balance exploration and exploitation in real time website optimisation. I collaborate with several companies, including and BT (the latter through the NG-CDI project, funded by an EPSRC Prosperity Partnership). I also lead the EPSRC/NERC-funded Data Science of the Natural Environment (DSNE) project at Lancaster University. Prior to my position at Lancaster, I was a senior lecturer in the statistics group of the School of Mathematics, University of Bristol, where I was co-director of the EPSRC-funded cross-disciplinary decision-making research group at the University of Bristol. I was also a partner in the ALADDIN project, a large strategic partnership between BAE Systems and EPSRC, and involving researchers from Imperial College, Southampton, Oxford, Bristol and BAE Systems.

Selected Publications Show all 40 publications

Time-varying decision boundaries: insights from optimality analysis
Malhotra, G., Leslie, D.S., Ludwig, C.J.H., Bogacz, R. 06/2018
Review article

Using J-K-fold Cross Validation to Reduce Variance When Tuning NLP Models
Moss, H., Leslie, D.S., Rayson, P.E. 06/2018 In: Proceedings of COLING 2018. 12 p.
Conference contribution

Robustness Properties in Fictitious-Play-Type Algorithms
Swenson, B., Kar, S., Xavier, J., Leslie, D.S. 24/10/2017 In: SIAM Journal on Control and Optimization. 55, p. 3295-3318. 24 p.
Journal article

Mixed-strategy learning with continuous action sets
Perkins, S., Mertikopoulos, P., Leslie, D.S. 01/2017 In: IEEE Transactions on Automatic Control. 62, 1, p. 379-384. 6 p.
Journal article

REX: a development platform and online learning approach for Runtime emergent software systems
Porter, B.F., Grieves, M., Rodrigues Filho, R., Leslie, D.S. 2/11/2016 In: Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation. USENIX Association p. 333-348. 16 p. ISBN: 9781931971331.
Conference contribution

Optimistic Bayesian sampling in contextual-bandit problems
May, B.C., Korda, N., Lee, A., Leslie, D.S. 06/2012 In: Journal of Machine Learning Research. 13, p. 2069-2106. 37 p.
Journal article

DSI: Data Science of the Natural Environment
16/04/2018 → 15/04/2023

DSI : Contextual Bandits for Retail Pricing
01/03/2017 → 31/12/2018

Statistical Learning, STOR-i Centre for Doctoral Training

STOR-i Centre for Doctoral Training

STOR-i Centre for Doctoral Training

Statistical Learning, STOR-i Centre for Doctoral Training

Modelling and Inference, Statistical Learning, STOR-i Centre for Doctoral Training

  • Analysis and Probability
  • Statistical Learning
  • STOR-i Centre for Doctoral Training