Since this is an event unlikely to occur that frequently, let me point out that Université Paris-Dauphine got a nominal mention in Nature of two weeks ago, through an article covering the recent Abel Prize of Yves Meyer and his work on wavelets through a collection of French institutions, including Paris-Dauphine where he was a professor in the maths department (CEREMADE) from 1985 till 1996. (Except for including a somewhat distantly related picture of an oscilloscope and a mention of the Higgs boson, the Nature article is quite nice!)
Archive for Université Paris Dauphine
A news report last weekend on Nature webpage about the new science super-campus south of Paris connected with my impressions of the whole endeavour: the annual report from the Court of Auditors estimated that the 5 billion euros invested in this construct were not exactly a clever use of public [French taxpayer] money! This notion to bring a large number of [State] engineer and scientific schools from downtown Paris to the plateau of Saclay, about 25km south-west of Paris, around École Polytechnique, had some appeal, since these were and are prestigious institutions, most with highly selective entry exams, and with similar training programs, now that they have almost completely lost the specialisation that justified their parallel existences! And since a genuine university, Paris 11 Orsay, stood nearby at the bottom of the plateau. Plus, a host of startups and research branches of companies. Hence the concept of a French MIT.
However, as so often the case in Jacobin France, the move has been decided and supported by the State “top-down” rather than by the original institutions themselves. Including a big push by Nicolas Sarkozy in 2010. While the campus can be reached by public transportation like RER, the appeal of living and working on the campus is obviously less appealing to both students and staff than in a listed building in the centre of Paris. Especially when lodging and living infrastructures are yet to be completed. But the main issue is that the fragmentation of those schools, labs and institutes, in terms of leadership, recruiting, research, and leadership, has not been solved by the move, each entity remaining strongly attached to its identity, degree, networks, &tc., and definitely unwilling to merge into a super-university with a more efficient organisation of teaching and research. Which means the overall structure as such is close to invisible at the international level. This is the point raised by the State auditors. And perceived by the State which threatens to cut funding at this late stage!
This is not the only example within French higher educations institutions since most have been forced to merged into incomprehensible super-units under the same financial threat. Like Paris-Dauphine being now part of the PSL (Paris Sciences et Lettres) heterogeneous conglomerate. (I suspect one of the primary reasons for this push by central authorities was to create larger entities towards moving up in the international university rankings, which is absurd for many reasons, from the limited worth of such rankings, to the lag between the creation of a new entity and the appearance on an international university ranking, to the difficulty in ranking researchers from such institutions: in Paris-Dauphine, the address to put on papers is more than a line long, with half a dozen acronyms!)
Today, I alas missed a seminar at BiPS on the Zig-Zag (sub-)sampler of Joris Bierkens, Paul Fearnhead and Gareth Roberts, presented here in Paris by James Ridgway. Fortunately for me, I had some discussions with Murray Pollock in Warwick and then again with Changye Wu in Dauphine that shed some light on this complex but highly innovative approach to simulating in Big Data settings thanks to a correct subsampling mechanism.
The zig-zag process runs a continuous process made of segments that turn from one diagonal to the next at random times driven by a generator connected with the components of the gradient of the target log-density. Plus a symmetric term. Provided those random times can be generated, this process is truly available and associated with the right target distribution. When the components of the parameter are independent (an unlikely setting), those random times can be associated with an inhomogeneous Poisson process. In the general case, one needs to bound the gradients by more manageable functions that create a Poisson process that can later be thinned. Next, one needs to simulate the process for the upper bound, a task that seems hard to achieve apart from linear and piecewise constant upper bounds. The process has a bit of a slice sampling taste, except that it cannot be used as a slice sampler but requires continuous time integration, given that the length of each segment matters. (Or maybe random time subsampling?)
A highly innovative part of the paper concentrates on Big Data likelihoods and on the possibility to subsample properly and exactly the original dataset. The authors propose Zig-Zag with subsampling by turning the gradients into random parts of the gradients. While remaining unbiased. There may be a cost associated with this gain of one to n, namely that the upper bounds may turn larger as they handle all elements in the likelihood at once, hence become (even) less efficient. (I am more uncertain about the case of the control variates, as it relies on a Lipschitz assumption.) While I still miss an easy way to implement the approach in a specific model, I remain hopeful for this new approach to make a major dent in the current methodologies!
The name of Ilya Sobol is familiar to researchers in quasi-Monte Carlo methods for his Sobol’s sequences. I was thus surprised to find in my office a small book entitled The Monte Carlo Method by this author, which is a translation of his 1968 book in Russian. I have no idea how it reached my office and I went to check with the library of Paris-Dauphine around the corner [of my corridor] whether it had been lost: apparently, the library got rid of it among a collection of old books… Now, having read through this 67 pages book (or booklet as Sobol puts it) makes me somewhat agree with the librarians, in that there is nothing of major relevance in this short introduction. It is quite interesting to go through the book and see the basics of simulation principles and Monte Carlo techniques unfolding, from the inverse cdf principle [established by a rather convoluted proof] to importance sampling, but the amount of information is about equivalent to the Wikipedia entry on the topic. From an historical perspective, it is also captivating to see the efforts to connect physical random generators (such as those based on vacuum tube noise) to shift-register pseudo-random generators created by Sobol in 1958. On a Soviet Strela computer.
While Googling the title of that book could not provide any connection, I found out that a 1994 version had been published under the title of A Primer for the Monte Carlo Method, which is mostly the same as my version, except for a few additional sections on pseudo-random generation, from the congruential method (with a FORTRAN code) to the accept-reject method being then called von Neumann’s instead of Neyman’s, to the notion of constructive dimension of a simulation technique, which amounts to demarginalisation, to quasi-Monte Carlo [for three pages]. A funny side note is that the author notes in the preface that the first translation [now in my office] was published without his permission!
For ‘Og’s readers interested in lecturer or professor positions in French universities next academic year, including a lecturer position at Paris-Dauphine in applied and computational statistics!, you need to apply for a qualification label by a national committee which strict deadline is next Tuesday, October 25, at 4pm (Paris/CET time). (The whole procedure is exclusively in French!)
This is the cover page of Marco Banterle‘s thesis, who will defend on Thursday [July 21, 13:00], at a rather quiet time for French universities, which is one reason for advertising it here. The thesis is built around several of Marco’s papers, like delayed acceptance, dimension expansion, and Gaussian copula for graphical models. The defence is open to everyone, so feel free to join if near Paris-Dauphine!