Dear all, Dr. Botond Szabo from the Mathematics department at Leiden university (The Netherlands), would be visiting the school from the 21 to the 29th of April 2017. His research interests covers Nonparametric Bayesian Statistics, Adaptation, Asymptotic Statistics, Operation research and Graph Theory. He has kindly accepted to give a two tutorials (90 minutes each) about recovery and uncertainty quantification in nonparametric models; and about high dimensional inference and uncertainty quantification. The tutorials will take place in the 535 room (Carslaw Building, 5th floor) Monday 24th April at 11am and Wednesday the 26th of April at 11am. He will also give a seminar on Friday 28th April (see details below) in Carslaw 173. Hope to see many of you there and I would encourage PhD students to attend the tutorials and the seminar. Kind regards, Lamiae. ############################################################################## Title: An asymptotic analysis of nonparametric distributed methods Abstract In the recent years in certain applications datasets have become so large that it becomes unfeasible, or computationally undesirable, to carry out the analysis on a single machine. This gave rise to divide-and-conquer algorithms where the data is distributed over several "local" machines and the computations are done on these machines parallel to each other. Then the outcome of the local computations are somehow aggregated to a global result in a central machine. Over the years various divide-and-conquer algorithms were proposed, many of them with limited theoretical underpinning. First we compare the theoretical properties of a (not complete) list of proposed methods on the benchmark nonparametric signal-in-white-noise model. Most of the investigated algorithms use information on aspects of the underlying true signal (for instance regularity), which is usually not available in practice. A central question is whether one can tune the algorithms in a data-driven way, without using any additional knowledge about the signal. We show that (a list of) standard data-driven techniques (both Bayesian and frequentist) can not recover the underlying signal with the minimax rate. This, however, does not imply the non-existence of an adaptive distributed method. To address the theoretical limitations of data-driven divide-and-conquer algorithms we consider a setting where the amount of information sent between the local and central machines is expensive and limited. We show that it is not possible to construct data-driven methods which adapt to the unknown regularity of the underlying signal and at the same time communicates the optimal amount of information between the machines. This is a joint work with Harry van Zanten. About the speaker: Botond Szabo is an Assistant Professor at the University of Leiden, The Netherlands. Botond received his phd in Mathematical Statistics from the Eindhoven University of technology, the Netherlands in 2014 under the supervision of Prof.dr. Harry van Zanten and Prof.dr. Aad van der Vaart. His research interests cover Nonparametric Bayesian Statistics, Adaptation, Asymptotic Statistics, Operation research and Graph Theory. He received the Savage Award in Theory & Methods: Runner up for the best PhD dissertation in the field of Bayesian statistics and econometrics in the category Theory & Methods and the "Van Zwet Award" for the best PhD dissertation in the Netherlands in Statistics and Operation Research 2015. He is an Associate Editor of Bayesian Analysis. You can find more about him here: http://math.bme.hu/~bszabo/index_en.html .