Archive for École Polytechnique

data science summer school à l’X

Posted in Statistics with tags , , , , , , on January 10, 2019 by xi'an

bâtiment Alan Turing [jatp]

Posted in pictures, Travel, University life with tags , , , , , on June 24, 2018 by xi'an

[ex?] Paris-Saclay univerXity

Posted in Kids, pictures, University life with tags , , , , , , , , , on November 5, 2017 by xi'an

In the plane to Warwick last Tuesday, I read a fairly long and pessimistic article in Le Monde about the future of the Paris-Saclay university. This debate presumably makes no sense outside French circles, for it relates to the century-old opposition between universities and grandes écoles, these selective engineering and business schools that operate independently from the university structure. In the sense that the selection and the schooling of their students is completely separated, meaning these students may never attend a university program if they choose to do so. But not so independently in terms of hiring part-time professors from universities and sharing resources for their Master programs. And depending on the same agencies (like CNRS) for funding their research program.

Anyway, the core message of this article was that the influence of former students from Polytechnique [aka X] in the high administration is such that they can prevent the integration of the different engineer schools on the Saclay plateau into the intended superstructure of the Paris-Saclay university. And turn this somewhat megalomanic Paris-Saclay project initiated by Nicolas Sarkozy into a mere geographical superposition of separate institutions, with very unequal State funding, perpetuating the two speed regime for public higher education… And a ever more confusing international image that will not help an inch moving up the Shanghai ranking (a major reason for Sarkozy pushing this project). Very French indeed!

a position served on a plate [ENSAE ParisTech, France]

Posted in Kids, pictures, Statistics, Travel, University life with tags , , , , , , , , , , on October 6, 2017 by xi'an

Nicolas Chopin emailed me about the opening of a position of Professor of Statistics at [my alma mater] ENSAE, on the Paris-Saclay campus [and plateau], next to Polytechnique, Telecom, and a bunch of other engineer schools [a.k.a The French MIT!]. The largest concentration of Science majors in France, definitely to be considered for a posiiton in France! Deadline is quite soon, as November 1. [Pardon my French: The pun in the title sort of fizzled out in translation because served on a plate is equivalent to served on a plateau in French.]

Hamiltonian MC on discrete spaces

Posted in Statistics, Travel, University life with tags , , , , , , , , on July 3, 2017 by xi'an

Following a lively discussion with Akihiko Nishimura during a BNP11 poster session last Tuesday, I took the opportunity of the flight to Montréal to read through the arXived paper (written jointly with David Dunson and Jianfeng Liu). The issue is thus one of handling discrete valued parameters in Hamiltonian Monte Carlo. The basic “trick” in handling this complexity goes by turning the discrete support via the inclusion of an auxiliary continuous variable whose discretisation is the discrete parameter, hence resembling to some extent the slice sampler. This removes the discreteness blockage but creates another difficulty, namely handling a discontinuous target density. (I idly wonder why the trick cannot be iterated to second or higher order so that to achieve the right amount of smoothness. Of course, the maths behind would be less cool!) The extension of the Hamiltonian to this setting by a  convolution is a trick I had not seen since the derivation of the Central Limit Theorem during Neveu’s course at Polytechnique.  What I find most exciting in the resolution is the move from a Gaussian momentum to a Laplace momentum, for the reason that I always wondered at alternatives [without trying anything myself!]. The Laplace version is indeed most appropriate here in that it avoids a computation of all discontinuity points and associated values along a trajectory. Since the moves are done component-wise, the method has a Metropolis-within-Gibbs flavour, which actually happens to be a special case. What is also striking is that the approach is both rejection-free and exact, provided ergodicity occurs, which is the case when the stepsize is random.

In addition to this resolution of the discrete parameter problem, the paper presents the further appeal of (re-)running an analysis of the Jolly-Seber capture-recapture model. Where the discrete parameter is the latent number of live animals [or whatever] in the system at any observed time. (Which we cover in Bayesian essentials with R as a neat entry to both dynamic and latent variable models.) I would have liked to see a comparison with the completion approach of Jérôme Dupuis (1995, Biometrika), since I figure the Metropolis version implemented here differs from Jérôme’s. The second example is built on Bissiri et al. (2016) surrogate likelihood (discussed earlier here) and Chopin and Ridgway (2017) catalogue of solutions for not analysing the Pima Indian dataset. (Replaced by another dataset here.)