Professor Patrick Rebeschini

Professor of Statistics and Machine Learning

Biographical Sketch

I have a Ph.D. in Operations Research and Financial Engineering from Princeton University (2014). After that, I joined the Yale Institute for Network Science at Yale University. I worked two years as a Postdoctoral Associate in the Electrical Engineering Department, and one year as an Associate Research Scientist with a joint appointment as a Lecturer in the Computer Science Department at Yale.

Research Interests

My research interests lie at the intersection of probability, statistics, and computer science. I am interested in the investigation of fundamental principles in high-dimensional probability, statistics and optimisation to design computationally efficient and statistically optimal algorithms for machine learning.

Publications

Rebeschini, P. and Tatikonda, S. (2018) “Locality in network optimization”, IEEE Transactions on Control of Network Systems, 6(2), pp. 487–500.

Tatikonda, S. and Rebeschini, P. (2018) “Accelerated consensus via Min-Sum Splitting”, in Advances in Neural Information Processing Systems 30: 31st Annual Conference on Neural Information Processing Systems (NIPS 2017). Curran Associates, pp. 1375–1385.

Rebeschini, P. and Tatikonda, S. (2017) “Accelerated Consensus via Min-Sum Splitting”, arXiv.

Rebeschini, P. and Tatikonda, S. (2016) “A New Approach to Laplacian Solvers and Flow Problems”, arXiv.

Rebeschini, P. and Tatikonda, S. (2016) “Decay of Correlation in Network Flow Problems”, in. Institute of Electrical and Electronics Engineers (IEEE), pp. 169–174.

Rebeschini, P. and van Handel, R. (2015) “Can local particle filters beat the curse of dimensionality?”, The Annals of Applied Probability, 25(5), pp. 2809–2866.

Rebeschini, P. and Tatikonda, S. (2015) “Locality in Network Optimization”, arXiv.

Rebeschini, P. and Karbasi, A. (2015) “Fast Mixing for Discrete Point Processes”, arXiv.

Rebeschini, P. and van Handel, R. (2015) “Phase transitions in nonlinear filtering”, Electronic Journal of Probability, 20(none).

Rebeschini, P. and Karbasi, A. (2015) “Fast mixing for discrete point processes”, in Journal of Machine Learning Research.

Contact Details

College affiliation: Tutorial Fellow at University College

Email: patrick.rebeschini@stats.ox.ac.uk

Web: https://www.stats.ox.ac.uk/~rebeschi/

Research Groups

Statistical Theory and Methodology

Computational Statistics and Machine Learning