Archive for machine learning

Statistics month in Marseilles (CIRM)

Posted in Books, Kids, Mountains, pictures, Running, Statistics, Travel, University life, Wines with tags , , , , , , , , , , , , , , on June 24, 2015 by xi'an

Calanque de Morgiou, Marseille, July 7, 2010Next February, the fabulous Centre International de Recherche en Mathématiques (CIRM) in Marseilles, France, will hold a Statistics month, with the following programme over five weeks

Each week will see minicourses of a few hours (2-3) and advanced talks, leaving time for interactions and collaborations. (I will give one of those minicourses on Bayesian foundations.) The scientific organisers of the B’ week are Gilles Celeux and Nicolas Chopin.

The CIRM is a wonderful meeting place, in the mountains between Marseilles and Cassis, with many trails to walk and run, and hundreds of fantastic climbing routes in the Calanques at all levels. (In February, the sea is too cold to contemplate swimming. The good side is that it is not too warm to climb and the risk of bush fire is very low!) We stayed there with Jean-Michel Marin a few years ago when preparing Bayesian Essentials. The maths and stats library is well-provided, with permanent access for quiet working sessions. This is the French version of the equally fantastic German Mathematik Forschungsinstitut Oberwolfach. There will be financial support available from the supporting societies and research bodies, at least for young participants and the costs if any are low, for excellent food and excellent lodging. Definitely not a scam conference!

Paris Machine Learning Meeting #10 Season 2

Posted in Books, Kids, pictures, Statistics, University life with tags , , , , , , , , , , on June 17, 2015 by xi'an

Invalides, Paris, May 8, 2012

Tonight, I am invited to give a speed-presenting talk at the Paris Machine Learning last meeting of Season 2, with the themes of DL, Recovering Robots, Vowpal Wabbit, Predcsis, Matlab, and Bayesian test [by yours truly!] The meeting will take place in Jussieu, Amphi 25, Here are my slides for the meeting:

As it happened, the meeting  was quite crowded with talks and plagued with technical difficulties in transmitting talks from Berlin and Toronto, so I came to talk about three hours after the beginning, which was less than optimal for the most technical presentation of the evening. I actually wonder if I even managed to carry the main idea of replacing Bayes factors with posteriors of the mixture weight! [I had plenty of time to reflect upon this on my way back home as I had to wait for several and rare and crowded RER trains until one had enough room for me and my bike!]

Statistics and Computing special issue on BNP

Posted in Books, Statistics, University life with tags , , , , , , , on June 16, 2015 by xi'an

[verbatim from the call for papers:]

Statistics and Computing is preparing a special issue on Bayesian Nonparametrics, for publication by early 2016. We invite researchers to submit manuscripts for publication in the special issue. We expect that the focus theme will increase the visibility and impact of papers in the volume.

By making use of infinite-dimensional mathematical structures, Bayesian nonparametric statistics allows the complexity of a learned model to grow as the size of a data set grows. This flexibility can be particularly suited to modern data sets but can also present a number of computational and modelling challenges. In this special issue, we will showcase novel applications of Bayesian nonparametric models, new computational tools and algorithms for learning these models, and new models for the diverse structures and relations that may be present in data.

To submit to the special issue, please use the Statistics and Computing online submission system. To indicate consideration for the special issue, choose “Special Issue: Bayesian Nonparametrics” as the article type. Papers must be prepared in accordance with the Statistics and Computing journal guidelines.

Papers will go through the usual peer review process. The special issue website will be updated with any relevant deadlines and information.

Deadline for manuscript submission: August 20, 2015

Guest editors:
Tamara Broderick (MIT)
Katherine Heller (Duke)
Peter Mueller (UT Austin)

importance weighting without importance weights [ABC for bandits?!]

Posted in Books, Statistics, University life with tags , , , , on March 27, 2015 by xi'an

I did not read very far in the recent arXival by Neu and Bartók, but I got the impression that it was a version of ABC for bandit problems where the probabilities behind the bandit arms are not available but can be generated. Since the stopping rule found in the “Recurrence weighting for multi-armed bandits” is the generation of an arm equal to the learner’s draw (p.5). Since there is no tolerance there, the method is exact (“unbiased”). As no reference is made to the ABC literature, this may be after all a mere analogy…

Significance and artificial intelligence

Posted in Books, Kids, pictures, Statistics, University life with tags , , , , , , , , , , , , , on March 19, 2015 by xi'an

As my sorry excuse of an Internet provider has been unable to fix my broken connection for several days, I had more time to read and enjoy the latest Significance I received last week. Plenty of interesting entries, once again! Even though, faithful to my idiosyncrasies, I must definitely criticise the cover (but you may also skip till the end of the paragraph!): It shows a pile of exams higher than the page frame on a student table in a classroom and a vague silhouette sitting behind the exams. I do not know whether or not this is intentional but the silhouette has definitely been added to the original picture (and presumably the exams as well!), because the seat and blackboard behind this silhouette show through it. If this is intentional, does that mean that the poor soul grading this endless pile of exams has long turned into a wraith?! If not intentional, that’s poor workmanship for a magazine usually apt at making the most from the graphical side. (And then I could go on and on about the clearly independent choice of illustrations by the managing editor rather than the author(s) of the article…) End of the digression! Or maybe not because there also was an ugly graph from Knowledge is Beautiful about the causes of plane crashes that made pie-charts look great… Not that all the graphs in the book are bad, far from it!

“The development of full artificial intelligence could spell the end of the human race.’ S. Hawkins

The central theme of the magazine is artificial intelligence (and machine learning). A point I wanted to mention in a post following the recent doom-like messages of Gates and Hawking about AIs taking over humanity à la Blade Runner… or in Turing’s test. As if they had not already impacted our life so much and in so many ways. And no all positive or for the common good. Witness the ultra-fast codes on the stock market. Witness the self-replicating and modifying computer viruses. Witness the increasingly autonomous military drones. Or witness my silly Internet issue, where I cannot get hold of a person who can tell me what the problem is and what the company is doing to solve it (if anything!), but instead have to listen to endless phone automata that tell me to press “1 if…” and “3 else”, and that my incident ticket has last been updated three days ago… But at the same time the tone of The Independent tribune by Hawking, Russell, Tegmark, and Wilczek is somewhat misguided, if I may object to such luminaries!, and playing on science fiction themes that have been repeated so many times that they are now ingrained, rather than strong scientific arguments. Military robots that could improve themselves to the point of evading their conceptors are surely frightening but much less realistic than a nuclear reaction that could not be stopped in a Fukushima plant. Or than the long-term impacts of genetically modified crops and animals. Or than the current proposals of climate engineering. Or than the emerging nano-particles.

“If we build systems that are game-theoretic or utility maximisers, we won’t get what we’re hoping for.” P. Norvig

The discussion of this scare in Significance does not contribute much in my opinion. It starts with the concept of a perfect Bayesian agent, supposedly the state of an AI creating paperclips, which (who?) ends up using the entire Earth’s resources to make more paperclips. The other articles in this cover story are more relevant, as for instance how AI moved from pure logic to statistical or probabilist intelligence. With Yee Whye Teh discussing Bayesian networks and the example of Google translation (including a perfect translation into French of an English sentence).

ABC à Montréal

Posted in Kids, pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , on December 13, 2014 by xi'an

Montreal1So today was the NIPS 2014 workshop, “ABC in Montréal“, which started with a fantastic talk by Juliane Liepe on some exciting applications of ABC to the migration of immune cells, with the analysis of movies involving those cells acting to heal a damaged fly wing and a cut fish tail. Quite amazing videos, really. (With the great entry line of ‘We have all cut  a finger at some point in our lives’!) The statistical model behind those movies was a random walk on a grid, with different drift and bias features that served as model characteristics. Frank Wood managed to deliver his talk despite a severe case of food poisoning, with a great illustration of probabilistic programming that made me understand (at last!) the very idea of probabilistic programming. And  Vikash Mansinghka presented some applications in image analysis. Those two talks led me to realise why probabilistic programming was so close to ABC, with a programming touch! Hence why I was invited to talk today! Then Dennis Prangle exposed his latest version of lazy ABC, that I have already commented on the ‘Og, somewhat connected with our delayed acceptance algorithm, to the point that maybe something common can stem out of the two notions. Michael Blum ended the day with provocative answers to the provocative question of Ted Meeds as to whether or not machine learning needed ABC (Ans. No!) and whether or not machine learning could help ABC (Ans. ???). With an happily mix-up between mechanistic and phenomenological models that helped generating discussion from the floor.

The posters were also of much interest, with calibration as a distance measure by Michael Guttman, in continuation of the poster he gave at MCMski, Aaron Smith presenting his work with Luke Bornn, Natesh Pillai and Dawn Woodard, on why a single pseudo-sample is enough for ABC efficiency. This gave me the opportunity to discuss with him the apparent contradiction with the result of Kryz Łatunsziński and Anthony Lee about the geometric convergence of ABC-MCMC only attained with a random number of pseudo-samples… And to wonder if there is a geometric versus binomial dilemma in this setting, Namely, whether or not simulating pseudo-samples until one is accepted would be more efficient than just running one and discarding it in case it is too far. So, although the audience was not that large (when compared with the other “ABC in…” and when considering the 2500+ attendees at NIPS over the week!), it was a great day where I learned a lot, did not have a doze during talks (!), [and even had an epiphany of sorts at the treadmill when I realised I just had to take longer steps to reach 16km/h without hyperventilating!] So thanks to my fellow organisers, Neil D Lawrence, Ted Meeds, Max Welling, and Richard Wilkinson for setting the program of that day! And, by the way, where’s the next “ABC in…”?! (Finland, maybe?)

reliable ABC model choice via random forests

Posted in pictures, R, Statistics, University life with tags , , , , , , , on October 29, 2014 by xi'an

human_ldaAfter a somewhat prolonged labour (!), we have at last completed our paper on ABC model choice with random forests and submitted it to PNAS for possible publication. While the paper is entirely methodological, the primary domain of application of ABC model choice methods remains population genetics and the diffusion of this new methodology to the users is thus more likely via a media like PNAS than via a machine learning or statistics journal.

When compared with our recent update of the arXived paper, there is not much different in contents, as it is mostly an issue of fitting the PNAS publication canons. (Which makes the paper less readable in the posted version [in my opinion!] as it needs to fit the main document within the compulsory six pages, relegated part of the experiments and of the explanations to the Supplementary Information section.)


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