Michael dans le Monde [#2]

A (second) back-page interview of Mike in Le Monde on the limitations of academics towards working with major high tech companies. And fatal attractions that are difficult to resist, given the monetary rewards. As his previous interview, this is quite an interesting read (in French), although it obviously reflects a US perspective rather than French (with the same comment applying to the recent interview of Yann LeCun on France Inter).

“…les chercheurs académiques français, qui sont vraiment très peu payés.”

The first part is a prediction that the GAFAs will not continue hiring (full-time or part-time) academic researchers to keep doing their academic research as the quest for more immediate profits will eventually win over the image produced by these collaborations. But maybe DeepMind is not the best example, as e.g. Amazon seems to be making immediate gains from such collaborations.

“…le modèle économique [de Amazon, Ali Baba, Uber, &tc] cherche à créer des marchés nouveaux avec à la source, on peut l’espérer, de nouveaux emplois.”

One stronger point of disagreement is about the above quote, namely that Uber or Amazon indeed create jobs. As I am uncertain that all jobs creations are worthwhile. Indeed, which kind of freedom there is in working after-hours for a reward that is so much below the minimal wage (in countries where there is a true minimal wage) that the workers [renamed entrepreneurs] are below the poverty line? Similarly, unless there are stronger regulations imposed by states or unions like the EU, it seems difficult to imagine how society as an aggregate of individuals can curb the hegemonic tendencies of the high tech leviathans…?

ICM 2018

While I am not following the International Congress of Mathematicians which just started in Rio, and even less attending, I noticed an entry on their webpage on my friend and colleague Maria Esteban which I would have liked to repost verbatim but cannot figure how. (ICM 2018 also features a plenary lecture by Michael Jordan on gradient based optimisation [which was also Michael’s topic at ISBA 2018] and another one by Sanjeev Arora on the maths deep learning, two talks broadly related with statistics, which is presumably a première at this highly selective maths conference!)

I am going to take the train next week! [prediction]

“…n’importe quelle personne appréciant le film Rashomon d’Akira Kurosawa, allant au travail en vélo et aimant le couscous va prendre le train la semaine prochaine”

In the Sciences & Médecine booklet of Le Monde this week, I found an interview of Michael Jordan on big data, under the title [I translated as] “We can always twist data the way we want”. (En français, bien sûr!) The content of the interview is great, not only because it comes after a series of poor quality articles on the “big data” revolution, but also because it sets statistics and induction at the centre of the analysis. I also liked the reference to Voltaire and transversal competences as fundamental. (Presumably this interview was done when Michael took part in a “big data” conference last month.) But what I appreciated most is the above quote that a person who likes Rashômon, bikes to work, and appreciates couscous should take a train next week! Michael intended it as a joke on the excesses of prediction, but as it happens, every single entry applies to me. Including taking a train to London at the end of next week…!

Michael Jordan’s course at CREST

Next month, Michael Jordan will give an advanced course at CREST-ENSAE, Paris, on Recent Advances at the Interface of Computation and Statistics. The course will take place on April 4 (14:00, ENSAE, Room #11), 11 (14:00, ENSAE, Room #11), 15 (11:00, ENSAE, Room #11) and 18 (14:00, ENSAE, Room #11). It is open to everyone and attendance is free. The only constraint is a compulsory registration with Nadine Guedj (email: guedj[AT]ensae.fr) for security issues. I strongly advise all graduate students who can take advantage of this fantastic opportunity to grasp it! Here is the abstract to the course:

“I will discuss several recent developments in areas where statistical science meets computational science, with particular concern for bringing statistical inference into contact with distributed computing architectures and with recursive data structures :

1. How does one obtain confidence intervals in massive data sets? The bootstrap principle suggests resampling data to obtain fluctuations in the values of estimators, and thereby confidence intervals, but this is infeasible computationally with massive data. Subsampling the data yields fluctuations on the wrong scale, which have to be corrected to provide calibrated statistical inferences. I present a new procedure, the “bag of little bootstraps,” which circumvents this problem, inheriting the favorable theoretical properties of the bootstrap but also having a much more favorable computational profile.

2. The problem of matrix completion has been the focus of much recent work, both theoretical and practical. To take advantage of distributed computing architectures in this setting, it is natural to consider divide-and-conquer algorithms for matrix completion. I show that these work well in practice, but also note that new theoretical problems arise when attempting to characterize the statistical performance of these algorithms. Here the theoretical support is provided by concentration theorems for random matrices, and I present a new approach to matrix concentration based on Stein’s method.

3. Bayesian nonparametrics involves replacing the “prior distributions” of classical Bayesian analysis with “prior stochastic processes.” Of particular value are the class of “combinatorial stochastic processes,” which make it possible to express uncertainty (and perform inference) over combinatorial objects that are familiar as data structures in computer science.”

References are available on Michael’s homepage.

reading classics (#4)

Another read today and not from JRSS B for once, namely,  Efron‘s (an)other look at the Jackknife, i.e. the 1979 bootstrap classic published in the Annals of Statistics. My Master students in the Reading Classics Seminar course thus listened today to Marco Brandi’s presentation, whose (Beamer) slides are here:

In my opinion this was an easier paper to discuss, more because of its visible impact than because of the paper itself, where the comparison with the jackknife procedure does not sound so relevant nowadays. again mostly algorithmic and requiring some background on how it impacted the field. Even though Marco also went through Don Rubin’s Bayesian bootstrap and Michael Jordan bag of little bootstraps, he struggled to get away from the technicality towards the intuition and the relevance of the method. The Bayesian bootstrap extension was quite interesting in that we discussed a lot the connections with Dirichlet priors and the lack of parameters that sounded quite antagonistic with the Bayesian principles. However, at the end of the day, I feel that this foundational paper was not explored in proportion to its depth and that it would be worth another visit.