Michael Osborne

Associate Professor in Machine Learning

Michael A Osborne (DPhil Oxon) is an expert in the development of intelligent algorithms capable of making sense of complex big data. His work in Machine Learning and non-parametric data analytics has been successfully applied in diverse and challenging contexts. For example, in astrostatistics, Dr Osborne’s probabilistic algorithms have aided the detection of planets in distant solar systems, and in autonomous robotics, his work has enabled self-driving cars to determine when their maps may have changed due to roadworks. More recently, he has addressed key societal challenges, analysing how intelligent algorithms might soon substitute for human workers, and predicting the resulting impact on employment. Michael A Osborne is an Associate Professor in Machine Learning, an Official Fellow of Exeter College, and a Faculty Member of the Oxford-Man Institute for Quantitative Finance, all at the University of Oxford.  

Related Events

Second Oxford-Man Institute Machine Learning Workshop
10th Anniversary Oxford-Man Institute Annual Workshop

Working Paper

Calliess, J., Osborne, M. and Roberts, S.J. (2013). Multi-agent planning with mixed-integer programming and adaptive interaction constraint generation.
Gillani, N., Eynon, R., Osborne, M., Hjorth, I. and Roberts, S. (2015). Communication communities in MOOCs.
Gunter, T., Lloyd, C., Osborne, M. and Roberts, S. (2015). Variational inference for Gaussian process modulated Poisson processes.
Rizvi, S., van Heerden, E., Salas, A., Nyikosa, F., Roberts, S., Osborne, M. and Rodriguez, E. (2017). Identifying sources of discrimination risk in the life cycle of machine intelligence applications under new European Union regs.
Fitzsimons, J.K., Osborne, M., Roberts, S. and Fitzsimons, J.F. (2016). Improved stochastic trace estimation using mutually unbiased bases.
Lloyd, C., Gunter, T., Osborne, M., Roberts, S. and Nickson, T (2016). Latent point process allocation.
Nyikosa, F., Osborne, M. and Roberts, S (2015). Adaptive Bayesian optimisation for online portfolio selection (NIPS).
Fitzsimons, J., Cutajar, K., Osborne, M., Roberts, S. and Filippone, M (2017). Bayesian inference of log determinants.
McLeod, M., Osborne, M. and Roberts, S (2017). Practical Bayesian Optimization for Variable Cost Objectives.
Bewsher, J., Tosi, A., Osborne, M. and Roberts, S (2017). Distribution of Gaussian process arc lengths. Proceedings of AISTATS.
Rizvi, S.A.A., Roberts, S.J., Osborne, M.A., and Nyikosa, F. (2017). A novel approach to forecasting financial volatility with Gaussian process envelopes.
Fitzsimons, J., Granziol, D., Cutajar, K., Osborne, M., Filippone, M. and Roberts, S. (2017). Entropic trace estimates for log determinants.
Salas, A., Roberts, S. and Osborne, M.A. (2015). A variational Bayesian state-space approach to online passive-aggressive regression.
Rizvi, S.A.A., Roberts, S.J., Osborne, M.A. and Nyikosa, F. (2017). Predicting dynamic renyi entropy using gaussian processes to estimate financial information flows.

Published Research

Calliess, J., Osborne, M. and Roberts, S.J. (2013). Nonlinear adaptive hybrid control by combining Gaussian process system identification with classical control laws. Novel Methods for Learning and Optimization of Control Policies and Trajectories for Robotics. ICRA.
Calliess, J., Osborne, M. and Roberts, S.J. (2013). Multi-agent planning with mixed-integer programming and adaptive interaction constraint generation. Sixth Annual Symposium on Combinatorial Search. SoCs. 2013.
Knowles-Cutler, A., Frey, C.B. and Osborne, M.A. (2014). Agiletown: the relentless march of technology and London’s response. Deloitte. TBC. 5-30.
Mann, R. et al. (2014). Objectively identifying landmark use and predicting flight trajectories of the homing pigeon using Gaussian processes. Journal of The Royal Society Interface. 8 (55). 210-219.
Gunter, T., Lloyd, C., Osborne, M.A. and Roberts, S.J. (2014). Efficient Bayesian nonparametric modelling of structured point processes. Proceedings of uncertainty in artificial intelligence (UAI). Forthcoming. Forthcoming.
Gunter, T., Osborne, M.A., Garnett, R., Hennig, P. and Roberts, S.J. (2014). Sampling for inference in probabilistic models with fast bayesian quadrature. Advances in Neural Information Processing Systems (NIPS). Forthcoming. Forthcoming.
Calliess, J., Osborne, M. and Roberts, S.J. (2014). Conservative collision prediction and avoidance for stochastic trajectories in continuous time and state. International Conference on Autonomous Agents and Multiagent Systems. N/A. N/A.
Calliess, J., Osborne, M. and Roberts, S.J. (2013). Nonlinear adaptive hybrid control by combining Gaussian process system identification with classical control laws. Novel Methods for Learning and Optimization of Control Policies and Trajectories for Robotics ICRA. N/A. N/A.
Calliess, J., Osborne, M. and Roberts, S.J. (2013). Multi-agent planning with mixed-integer programming and adaptive interaction constraint generation. Sixth Annual Symposium on Combinatorial Search SocS. -. -.
Ramchurn, S.D., Osborne, M.A., Parson, O., Rahwan, T., Maleki, S., Reece, S., Huynh, T.D., Alam, M., Fischer, J. and Rodden, T. (2013). AgentSwitch: towards smart electricity tariff selection. 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013). -. -.
Frey, C.B. and Osborne, M (2017). the future of employment: how susceptible are jobs to computerisation? .
Rajpaul, V., Aigrain, S., Osborne, M., Reece, S. and Roberts, S. (2015). A Gaussian process framework for modelling stellar activity signals in radial velocity data.