Archive for machine learning

Nature tidbits

Posted in Books, Statistics, University life with tags , , , , , , , , , , , on September 18, 2018 by xi'an

In the Nature issue of July 19 that I read in the plane to Singapore, there was a whole lot of interesting entries, from various calls expressing deep concern about the anti-scientific stance of the Trump administration, like cutting funds for environmental regulation and restricting freedom of communication (ETA) or naming a non-scientist at the head of NASA and other agencies, or again restricting the protection of species, to a testimony of an Argentinian biologist in front of a congressional committee about the legalisation of abortion (which failed at the level of the Agentinian senate later this month), to a DNA-like version of neural network, to Louis Chen from NUS being mentioned in a career article about the importance of planning well in advance one’s retirement to preserve academia links and manage a new position or even career. Which is what happened to Louis as he stayed head of NUS after the mandatory retirement age and is now emeritus and still engaged into research. (The article made me wonder however how the cases therein had be selected.) It is actually most revealing to see how different countries approach the question of retirements of academics: in France, for instance, one is essentially forced to retire and, while there exist emeritus positions, it is extremely difficult to find funding.

“Louis Chen was technically meant to retire in 2005. The mathematician at the National University of Singapore was turning 65, the university’s official retirement age. But he was only five years into his tenure as director of the university’s new Institute for Mathematical Sciences, and the university wanted him to stay on. So he remained for seven more years, stepping down in 2012. Over the next 18 months, he travelled and had knee surgery, before returning in summer 2014 to teach graduate courses for a year.”

And [yet] another piece on the biases of AIs. Reproducing earlier papers discussed here, with one obvious reason being that the learning corpus is not representative of the whole population, maybe survey sampling should become compulsory in machine learning training degrees. And yet another piece on why protectionism is (also) bad for the environment.

troubling trends in machine learning

Posted in Books, pictures, Running, Statistics, University life with tags , , , , , , , , , , , , , on July 25, 2018 by xi'an

This morning, in Coventry, while having an n-th cup of tea after a very early morning run (light comes early at this time of the year!), I spotted an intriguing title in the arXivals of the day, by Zachary Lipton and Jacob Steinhard. Addressing the academic shortcomings of machine learning papers. While I first thought little of the attempt to address poor scholarship in the machine learning literature, I read it with growing interest and, although I am pessimistic at the chances of inverting the trend, considering the relentless pace and massive production of the community, I consider the exercise worth conducting, if only to launch a debate on the excesses found in the literature.

“…desirable characteristics:  (i) provide intuition to aid the reader’s understanding, but clearly distinguish it from stronger conclusions supported by evidence; (ii) describe empirical investigations that consider and rule out alternative hypotheses; (iii) make clear the relationship between theoretical analysis and intuitive or empirical claims; and (iv) use language to empower the reader, choosing terminology to avoid misleading or unproven connotations, collisions with other definitions, or conflation with other related but distinct concepts”

The points made by the authors are (p.1)

  1. Failure to distinguish between explanation and speculation
  2. Failure to identify the sources of empirical gains
  3. Mathiness
  4. Misuse of language

Again, I had misgiving about point 3., but this is not an anti-maths argument, rather about the recourse to vaguely connected or oversold mathematical results as a way to support a method.

Most interestingly (and living dangerously!), the authors select specific papers to illustrate their point, picking from well-established authors and from their own papers, rather than from junior authors. And also include counter-examples of papers going the(ir) right way. Among the recommendations for emerging from the morass of poor scholarship papers, they suggest favouring critical writing and retrospective surveys (provided authors can be found for these!). And mention open reviews before I can mention these myself. One would think that published anonymous reviews are a step in the right direction, I would actually say that this should be the norm (plus or minus anonymity) for all journals or successors of journals (PCis coming strongly to mind). But requiring more work from the referees implies rewards for said referees, as done in some biology and hydrology journals I refereed for (and PCIs of course).

Bayesian gan [gan style]

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , , , , , on June 26, 2018 by xi'an

In their paper Bayesian GANS, arXived a year ago, Saatchi and Wilson consider a Bayesian version of generative adversarial networks, putting priors on both the model and the discriminator parameters. While the prospect seems somewhat remote from genuine statistical inference, if the following statement is representative

“GANs transform white noise through a deep neural network to generate candidate samples from a data distribution. A discriminator learns, in a supervised manner, how to tune its parameters so as to correctly classify whether a given sample has come from the generator or the true data distribution. Meanwhile, the generator updates its parameters so as to fool the discriminator. As long as the generator has sufficient capacity, it can approximate the cdf inverse-cdf composition required to sample from a data distribution of interest.”

I figure the concept can also apply to a standard statistical model, where x=G(z,θ) rephrases the distributional assumption x~F(x;θ) via a white noise z. This makes resorting to a prior distribution on θ more relevant in the sense of using potential prior information on θ (although the successes of probabilistic numerics show formal priors can be used on purely numerical ground).

The “posterior distribution” that is central to the notion of Bayesian GANs is however unorthodox in that the distribution is associated with the following conditional posteriors

where D(x,θ) is the “discriminator”, that is, in GAN lingo, the probability to be allocated to the “true” data generating mechanism rather than to the one associated with G(·,θ). The generative conditional posterior (1) then aims at fooling the discriminator, i.e. favours generative parameter values that raise the probability of wrong allocation of the pseudo-data. The discriminative conditional posterior (2) is a standard Bayesian posterior based on the original sample and the generated sample. The authors then iteratively sample from these posteriors, effectively implementing a two-stage Gibbs sampler.

“By iteratively sampling from (1) and (2) at every step of an epoch one can, in the limit, obtain samples from the approximate posteriors over [both sets of parameters].”

What worries me about this approach is that  just cannot work, in the sense that (1) and (2) cannot be compatible conditional (posterior) distributions. There is no joint distribution for which (1) and (2) would be the conditionals, since the pseudo-data appears in D for (1) and (1-D) in (2). This means that the convergence of a Gibbs sampler is at best to a stationary σ-finite measure. And hence that the meaning of the chain is delicate to ascertain… Am I missing any fundamental point?! [I checked the reviews on NIPS webpage and could not spot this issue being raised.]

rage against the [Nature] Machine [Intelligence]

Posted in Books, Statistics, University life with tags , , , , , , , , , on May 15, 2018 by xi'an

Yesterday evening, my friend and colleague Pierre Alquier (CREST-ENSAE) got interviewed (for a few seconds on-line!, around minute 06) by the French national radio, France Culture, about the recent call to boycott the incoming Nature Machine Intelligence electronic journal. Call to the machine learning community, based on the lack of paying journals among the major machine learnings journals, like JMLR. Meaning that related conferences like AISTATS and NIPS also get their accepted papers available on-line for free. As noted in the call

“Machine learning has been at the forefront of the movement for free and open access to research. For example, in 2001 the Editorial Board of the Machine Learning Journal resigned en masse to form a new zero-cost open access journal, the Journal of Machine Learning Research (JMLR).”

Imperial postdoc in Bayesian nonparametrics

Posted in pictures, R with tags , , , , , , , , on April 27, 2018 by xi'an

Here is another announcement for a post-doctoral position in London (UK) to work with Sarah Filippi. In the Department of Mathematics at Imperial College London. (More details on the site or in this document. Hopefully, the salary is sufficient for staying in London, if not in South Kensington!)

The post holder will work on developing a novel Bayesian Non-Parametric Test for Conditional Independence. This is at the core of modern causal discovery, itself of paramount importance throughout the sciences and in Machine Learning. As part of this project, the post holder will derive a Bayesian non-parametric testing procedure for conditional independence, scalable to high-dimensional conditioning variable. To ensure maximum impact and allow experimenters in different fields to easily apply this new methodology, the post holder will then create an open-source software package available on the R statistical programming platform. Doing so, the post holder will investigate applying this approach to real-world data from our established partners who have a track record of informing national and international bodies such as Public Health England and the World Health Organisation.

a postdoc with Christian Robert

Posted in Statistics with tags , , , , , , on April 12, 2018 by xi'an

[Here is another call for a postdoctoral position in Lyon, under the supervision of my homonym Christian Robert:]

Post-Doctoral Position in Data Science and Machine Learning

Chair Data Analytics and Models for Insurance – 20182020

DAMI is a research chair funded by BNP Paribas Cardif, and is interested in problems related to Data Science and Models for Insurance.

Description

A post-doctoral fellowship in the areas of data science and machine learning is available at the DAMI research chair at the Claude Bernard Lyon 1 University. The post-doctoral fellow, in collaboration with the industry partner, will conduct research of advanced analytics and machine learning algorithms for actuarial sciences and insurance, and in particular, for improving risk-based pricing, and for developing predictive analytics.

The position is for one year (possibly two years), starting in September/October 2018.

Required Professional Expertise

–        A recent PhD (received within the past 5 years) in Computer Science, Computer Engineering, Applied Statistics and Mathematics, or related fields.

–        Excellent research capabilities and competitive development skills

–        Strong knowledge/expertise in machine learning and data analytics including data pre-processing, model building, and model evaluation

–        Experience with Python and/or R

Interested candidates are invited to submit their CV, list of publications, and contact information of two references to Christian ROBERT at univ-lyon1.fr.

gender gaps

Posted in Statistics, University life with tags , , , , , , , , , , on March 31, 2018 by xi'an

Two of my colleagues [and co-authors] at Dauphine, Elyès Jouini and Clotilde Napp, published a paper in Science last week (and an associated tribune in Le Monde which I spotted first) about explaining differences in national gender inequalities in maths (as measured by PISA) in terms of the degree of overall inequality in the respective countries. Gaps in the highest maths performer sex ratio. While I have no qualm about the dependency or the overall statistical cum machine learning analysis (supported by our common co-author Jean-Michel Marin), and while I obviously know nothing about the topic!, I leisurely wonder at the cultural factor (which may also partly explain for the degree of inequality) when considering that the countries at the bottom of the above graphs are rather religious (and mostly catholic). I also find it most intriguing that the gender gap is consistently reversed when considering higher performer sex ratio for reading, because mastering the language should be a strong factor in power structures and hence differences therein should also lead to inequalities…