Archive for CREST
Here is a call from ENSAE about two positions in statistics/machine learning, starting next semester:
ENSAE ParisTech and CREST is currently inviting applications for one position at the level associate or full professor from outstanding candidates having demonstrated abilities in both research and teaching. We are interested in candidates with a Ph.D. in Statistics or Machine Learning (or related field) whose research interests are in high dimensional statistical inference, learning theory or statistics of networks.
The appointment could begin as soon as September 1, 2014. The position is for an initial three-year term, with a possible renewal option in case of positive evaluation of research and teaching activities. Salary for suitably qualified applicants is competitive and commensurate with experience. The deadline for application is May 19, 2014. Full details are given here for the first position and there for the second position.
Daniel Simpson gave a seminar at CREST yesterday on his recently arXived paper, “Penalising model component complexity: A principled, practical approach to constructing priors” written with Thiago Martins, Andrea Riebler, Håvard Rue, and Sigrunn Sørbye. Paper that he should also have given in Banff last month had he not lost his passport in København airport… I have already commented at length on this exciting paper, hopefully to become a discussion paper in a top journal!, so I am just pointing out two things that came to my mind during the energetic talk delivered by Dan to our group. The first thing is that those penalised complexity (PC) priors of theirs rely on some choices in the ordering of the relevance, complexity, nuisance level, &tc. of the parameters, just like reference priors. While Dan already wrote a paper on Russian roulette, there is also a Russian doll principle at work behind (or within) PC priors. Each shell of the Russian doll corresponds to a further level of complexity whose order need be decided by the modeller… Not very realistic in a hierarchical model with several types of parameters having only local meaning.
My second point is that the construction of those “politically correct” (PC) priors reflects another Russian doll structure, namely one of embedded models, hence would and should lead to a natural multiple testing methodology. Except that Dan rejected this notion during his talk, by being opposed to testing per se. (A good topic for one of my summer projects, if nothing more, then!)
Our newly created Chaire “Economie et gestion des nouvelles données” in Paris-Dauphine, ENS Ulm, École Polytechnique and ENSAE is recruiting a data scientist starting as early as May 1, the call remaining open till the position is filled. The location is in one of the above labs in Paris, the duration for at least one year, salary is varying, based on the applicant’s profile, and the contacts are Stephane Gaiffas (stephane.gaiffas AT cmap DOT polytechnique.fr), Robin Ryder (ryder AT ceremade DOT dauphine.fr). and Gabriel Peyré (peyre AT ceremade DOT dauphine.fr). Here are more details:
The chaire “Economie et gestion des nouvelles données” is recruiting a talented young engineer specialized in large scale computing and data processing. The targeted applications include machine learning, imaging sciences and finance. This is a unique opportunity to join a newly created research group between the best Parisian labs in applied mathematics and computer science (ParisDauphine, ENS Ulm, Ecole Polytechnique and ENSAE) working hand in hand with major industrial companies (Havas, BNP Paribas, Warner Bros.). The proposed position consists in helping researchers of the group to develop and implement large scale data processing methods, and applying these methods on real life problems in collaboration with the industrial partners.
A non exhaustive list of methods that are currently investigated by researchers of the group, and that will play a key role in the computational framework developed by the recruited engineer, includes :
● Large scale non smooth optimization methods (proximal schemes, interior points, optimization on manifolds).
● Machine learning problems (kernelized methods, Lasso, collaborative filtering, deep learning, learning for graphs, learning for timedependent systems), with a particular focus on large scale problems and stochastic methods.
● Imaging problems (compressed sensing, superresolution).
● Approximate Bayesian Computation (ABC) methods.
● Particle and Sequential Monte Carlo methods
The candidate should have a very good background in computer science with various programming environments (e.g. Matlab, Python, C++) and knowledge of high performance computing methods (e.g. GPU, parallelization, cloud computing). He/she should adhere to the open source philosophy and possibly be able to interact with the relevant communities (e.g. scikitlearn initiative). Typical curriculum includes engineering school or Master studies in computer science / applied maths / physics, and possibly a PhD (not required).
The recruited engineer will work within one of the labs of the chaire. He will benefit from a very stimulating working environment and all required computing resources. He will work in close interaction with the 4 research labs of the chaire, and will also have regular meetings with the industrial partners. More information about the chaire can be found online at http://www.di.ens.fr/~aspremon/chaire/
At MCMSki IV, I attended (and chaired) a session where Martyn Plummer presented some developments on cut models. As I was not sure I had gotten the idea [although this happened to be one of those few sessions where the flu had not yet completely taken over!] and as I wanted to check about a potential explanation for the lack of convergence discussed by Martyn during his talk, I decided to (re)present the talk at our “MCMSki decompression” seminar at CREST. Martyn sent me his slides and also kindly pointed out to the relevant section of the BUGS book, reproduced above. (Disclaimer: do not get me wrong here, the title is a pun on the infamous “drill, baby, drill!” and not connected in any way to Martyn’s talk or work!)
I cannot say I get the idea any clearer from this short explanation in the BUGS book, although it gives a literal meaning to the word “cut”. From this description I only understand that a cut is the removal of an edge in a probabilistic graph, however there must/may be some arbitrariness in building the wrong conditional distribution. In the Poisson-binomial case treated in Martyn’s case, I interpret the cut as simulating from
hence loosing some of the information about φ… Now, this cut version is a function of φ and θ that can be fed to a Metropolis-Hastings algorithm. Assuming we can handle the posterior on φ and the conditional on θ given φ. If we build a Gibbs sampler instead, we face a difficulty with the normalising constant m(y|φ). Said Gibbs sampler thus does not work in generating from the “cut” target. Maybe an alternative borrowing from the rather large if disparate missing constant toolbox. (In any case, we do not simulate from the original joint distribution.) The natural solution would then be to make a independent proposal on φ with target the posterior given z and then any scheme that preserves the conditional of θ given φ and y; “any” is rather wistful thinking at this stage since the only practical solution that I see is to run a Metropolis-Hasting sampler long enough to “reach” stationarity… I also remain with a lingering although not life-threatening question of whether or not the BUGS code using cut distributions provide the “right” answer or not. Here are my five slides used during the seminar (with a random walk implementation that did not diverge from the true target…):