Patrick Rebeschini

Patrick Rebeschini

Distributed machine learning

I am an Associate Professor in the Department of Statistics at the University of Oxford, and a Tutorial Fellow at University College, Oxford. I work on developing methodologies and theoretical foundations for Machine Learning.



  • P. Rebeschini , S. C. Tatikonda , Accelerated consensus via Min-Sum Splitting, in Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds. Curran Associates, Inc., 2017, 1374–1384.


  • P. Rebeschini , S. Tatikonda , Decay of correlation in network flow problems, in 2016 Annual Conference on Information Science and Systems (CISS), 2016, 169–174.


  • P. Rebeschini , R. Handel , Can local particle filters beat the curse of dimensionality?, Ann. Appl. Probab., vol. 25, no. 5, 2809–2866, Oct. 2015.
  • P. Rebeschini , R. Handel , Phase transitions in nonlinear filtering, Electron. J. Probab., vol. 20, 46 pp., 2015.
  • P. Rebeschini , A. Karbasi , Fast Mixing for Discrete Point Processes, in Proceedings of The 28th Conference on Learning Theory, Paris, France, 2015, vol. 40, 1480–1500.


  • P. Rebeschini , R. Handel , Comparison Theorems for Gibbs Measures, Journal of Statistical Physics, vol. 157, no. 2, 234–281, Oct. 2014.