Archive for machine learning

position opening at ENSAE ParisTech

Posted in Kids, Statistics, Travel, University life with tags , , , , , , , on March 28, 2016 by xi'an

ensaeprofParis and la Seine, from Pont du Garigliano, Oct. 20, 2011There is an opening for an associate or full professor position in Statistics and Machine Learning at ENSAE, Paris (soon to move to the Paris-Saclay campus, next to École Polytechnique). The details are provided here. The deadline is April 18, 2016, for a hiring in September or October 2016.

AISTATS 2016 [programme]

Posted in Books, Kids, pictures, Statistics, Travel, University life with tags , , , , , , , , on March 14, 2016 by xi'an

The full programme for AISTATS 2016 in Cádiz is now on-line, including the posters (except for the additional posters by MLSS participants). Richard Samworth is scheduled to talk on Monday morning, May 9, Kamalika Chaudhuri on Tuesday morning, May 10, and Adam Tauman Kalai  on Wednesday morning, May 11. As at the previous AISTATS meeting, poster sessions are central to the day, while evenings are free (which shows this is not a Bayesian meeting!!!). See you in Cádiz, hopefully! (Registration is still open, just in case.)

MLSS 2016: machine learning summer school in Cádiz [deadline]

Posted in Kids, pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , on March 11, 2016 by xi'an

Following [time-wise] the AISTATS 2016 meeting, a machine learning school is organised in Cádiz (as is the tradition for AISTATS meetings in Europe, i.e., in even years). With an impressive [if downright scary] poster! There is no strong statistics component in the programme, apart from a course by Tamara Broderick on non-parametric Bayes, but the list of speakers is impressive and the ten day school is worth recommending for all interested students.  (I remember giving a short course at MLSS 2004 on Berder Island in Brittany, with the immediate reward of running the Auray-Vannes half-marathon that year…) The deadline for applications is March 25, 2016.

go, go, go…deeper!

Posted in pictures, Statistics with tags , , , , , , , , , , on February 19, 2016 by xi'an

While visiting Warwick, last week, I came across the very issue of Nature with the highly advertised paper of David Silver and co-authors from DeepMind detailing how they designed their Go player algorithm that bested a European Go master five games in a row last September. Which is a rather unexpected and definitely brilliant feat given the state of the art! And compares (in terms of importance, if not of approach) with the victory of IBM Deep Blue over Gary Kasparov 20 years ago… (Another deep algorithm, showing that the attraction of programmers for this label has not died off over the years!)This paper is not the easiest to read (especially over breakfast), with (obviously) missing details, but I gathered interesting titbits from this cursory read. One being the reinforced learning step where the predictor is improved by being applied against earlier versions. While this can lead to overfitting, the authors used randomisation to reduce this feature. This made me wonder if a similar step could be on predictors like random forests. E.g., by weighting the trees or the probability of including a predictor or another.Another feature of major interest is their parallel use of two neural networks in the decision-making, a first one estimating a probability distribution over moves learned from millions of human Go games and a second one returning a utility or value for each possible move. The first network is used for tree exploration with Monte Carlo steps, while the second leads to the final decision.

This is a fairly good commercial argument for machine learning techniques (and for DeepMind as well), but I do not agree with the doom-sayers predicting the rise of the machines and our soon to be annihilation! (Which is the major theme of Superintelligence.) This result shows that, with enough learning data and sufficiently high optimising power and skills, it is possible to produce an excellent predictor of the set of Go moves leading to a victory. Without the brute force strategy of Deep Blue that simply explored the tree of possible games to a much more remote future than a human player could do (along with the  perfect memory of a lot of games). I actually wonder if DeepMind has also designed a chess algorithm on the same principles: there is no reason why it should no work. However, this success does not predict the soon to come emergence of AI’s able to deal with vaguer and broader scopes: in that sense, automated drivers are much more of an advance (unless they start bumping into other cars and pedestrians on a regular basis!).

Bayesian composite likelihood

Posted in Books, Statistics, University life with tags , , , , , , on February 11, 2016 by xi'an

“…the pre-determined weights assigned to the different associations between observed and unobserved values represent strong a priori knowledge regarding the informativeness of clues. A poor choice of weights will inevitably result in a poor approximation to the “true” Bayesian posterior…”

Last Xmas, Alexis Roche arXived a paper on Bayesian inference via composite likelihood. I find the paper quite interesting in that [and only in that] it defends the innovative notion of writing a composite likelihood as a pool of opinions about some features of the data. Recall that each term in the composite likelihood is a marginal likelihood for some projection z=f(y) of the data y. As in ABC settings, although it is rare to derive closed-form expressions for those marginals. The composite likelihood is parameterised by powers of those components. Each component is associated with an expert, whose weight reflects the importance. The sum of the powers is constrained to be equal to one, even though I do not understand why the dimensions of the projections play no role in this constraint. Simplicity is advanced as an argument, which sounds rather weak… Even though this may be infeasible in any realistic problem, it would be more coherent to see the weights as producing the best Kullback approximation to the true posterior. Or to use a prior on the weights and estimate them along the parameter θ. The former could be incorporated into the later following the approach of Holmes & Walker (2013). While the ensuing discussion is most interesting, it remains missing in connecting the different components in terms of the (joint) information brought about the parameters. Especially because the weights are assumed to be given rather than inferred. Especially when they depend on θ. I also wonder why the variational Bayes interpretation is not exploited any further. And see no clear way to exploit this perspective in an ABC environment.

conference deadlines

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , on January 22, 2016 by xi'an

Just to remind participants to AISTATS 2016 and to ISBA 2016 that the deadlines for early registration are January 31 and February 10, getting close. Since both fees are quite high, it certainly makes sense to take advantage of those deadlines (and to make all travel reservations). (While I did try to see the fees of AISTATS 2016 set to a lower value, half of the fees are paying for coffee breaks and the banquet and the welcome party and were not negotiable. As my suggestion of cancelling the banquet was not accepted either! At least, the offer of accommodations in Cadiz is reasonable, from the list of hotels on the website, to a large collection of airbnb listings [minus the one I just reserved!]. And both Spain and Italy set an heavy 20% tax on conferences… Warning: the AISTATS 2016 do not cover the shuttle bus transfer from Sevilla, the major airport in the vicinity.)

years (and years) of data science

Posted in Books, Statistics, Travel, University life with tags , , , , , , , , , , , , , , on January 4, 2016 by xi'an

In preparation for the round table at the start of the MCMSkv conference, this afternoon, Anto sent us a paper written by David Donoho for the Tukey Centennial workshop, held in Princeton last September. Entitled 50 years of Data Science. And which attracted a whole round of comments, judging from the Google search results. So much that I decided not to read any of them before parsing through the paper. But almost certainly reproducing here with my two cents some of the previous comments.

“John Tukey’s definition of `Big Data’ was `anything that won’t fit on one device’.”

The complaint that data science is essentially statistics that does not dare to spell out statistics as if it were a ten letter word (p.5) is not new, if appropriate. In this paper, David Donoho evacuates the memes that supposedly separate data science from statistics, like “big data” (although I doubt non-statisticians would accept the quick rejection that easily, wondering at the ability of statisticians to develop big models), skills like parallel programming (which ineluctably leads to more rudimentary algorithms and inferential techniques), jobs requiring such a vast array of skills and experience that no graduate student sounds properly trained for it…

“A call to action, from a statistician who fells `the train is leaving the station’.” (p.12)

One point of the paper is to see 1962 John Tukey’s “The Future of Data Analysis” as prophetical of the “Big Data” and “Data Science” crises. Which makes a lot of sense when considering the four driving forces advanced by Tukey (p.11):

  1. formal statistics
  2. advanced computing and graphical devices
  3. the ability to face ever-growing data flows
  4. its adoption by an ever-wider range of fields

“Science about data science will grow dramatically in significance.”

David Donoho then moves on to incorporate   Leo Breiman’s 2001 Two Cultures paper. Which separates machine learning and prediction from statistics and inference, leading to the “big chasm”! And he sees the combination of prediction with “common task framework” as the “secret sauce” of machine learning, because of the possibility of objective comparison of methods on a testing dataset. Which does not seem to me as the explanation for the current (real or perceived) disaffection for statistics and correlated attraction for more computer-related solutions. A code that wins a Kaggle challenge clearly has some efficient characteristics, but this tells me nothing of the abilities of the methodology behind that code. If any. Self-learning how to play chess within 72 hours is great, but is the principle behind able to handle go at the same level?  Plus, I remain worried about the (screaming) absence of model (or models) in predictive approaches. Or at least skeptical. For the same reason it does not help in producing a generic approach to problems. Nor an approximation to the underlying mechanism. I thus see nothing but a black box in many “predictive models”, which tells me nothing about the uncertainty, imprecision or reproducibility of such tools. “Tool evaluation” cannot be reduced to a final score on a testing benchmark. The paper concludes with the prediction that the validation of scientific methodology will solely be empirical (p.37). This leaves little ground if any for probability and uncertainty quantification, as reflected their absence in the paper.

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