Archive for machine learning

le grand amphithéâtre de l’Université de Lyon

Posted in Kids, pictures, Travel, University life with tags , , , , , , , , , , on May 5, 2017 by xi'an

The talks of the statlearn 2017 conference took place in an amazing 19th Century amphitheatre that looked in much better conditions than the corresponding amphitheatre of La Sorbonne. After checking on-line, I found that this place had suffered a major fire in 1999 and had been renovated since then.

The main wall features a very academic painting by Jean-Joseph Weerts representing a rhethoric competition in Lugdunum (the hill of the god Lug, in Latin), under the Roman emperor Caligula. (It is hard to imagine this was painted at the time of the Impressionist revolution!) Which creates a huge distraction from listening to the first talk when one enters this room, as there are many stories woven into the painting, including the fate of the poor rethoricians, thrown in the Rhône by the emperor’s guards!

machine learning and the future of realism

Posted in Books, Kids, Statistics, University life with tags , , , , , , , , on May 4, 2017 by xi'an

Giles and Cliff Hooker arXived a paper last week with this intriguing title. (Giles Hooker is an associate professor of statistics and biology at Cornell U, with an interesting blog on the notion of models, while Cliff Hooker is a professor of philosophy at Newcastle U, Australia.)

“Our conclusion is that simplicity is too complex”

The debate in this short paper is whether or not machine learning relates to a model. Or is it concerned with sheer (“naked”) prediction? And then does it pertain to science any longer?! While it sounds obvious at first, defining why science is more than prediction of effects given causes is much less obvious, although prediction sounds more pragmatic and engineer-like than scientific. (Furthermore, prediction has a somewhat negative flavour in French, being used as a synonym to divination and opposed to prévision.) In more philosophical terms, prediction offers no ontological feature. As for a machine learning structure like a neural network being scientific or a-scientific, its black box nature makes it much more the later than the former, in that it brings no explanation for the connection between input and output, between regressed and regressors. It further lacks the potential for universality of scientific models. For instance, as mentioned in the paper, Newton’s law of gravitation applies to any pair of weighted bodies, while a neural network built on a series of observations could not be assessed or guaranteed outside the domain where those observations are taken. Plus, would miss the simple square law established by Newton. Most fascinating questions, undoubtedly! Putting the stress on models from a totally different perspective from last week at the RSS.

As for machine learning being a challenge to realism, I am none the wiser after reading the paper. Utilising machine learning tools to produce predictions of causes given effects does not seem to modify the structure of the World and very little our understanding of it, since they do not bring explanation per se. What would lead to anti-realism is the adoption of those tools as substitutes for scientific theories and models.

oxwasp@amazon.de

Posted in Books, Kids, pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , on April 12, 2017 by xi'an

The reason for my short visit to Berlin last week was an OxWaSP (Oxford and Warwick Statistics Program) workshop hosted by Amazon Berlin with talks between statistics and machine learning, plus posters from our second year students. While the workshop was quite intense, I enjoyed very much the atmosphere and the variety of talks there. (Just sorry that I left too early to enjoy the social programme at a local brewery, Brauhaus Lemke, and the natural history museum. But still managed nice runs east and west!) One thing I found most interesting (if obvious in retrospect) was the different focus of academic and production talks, where the later do not aim at a full generality or at a guaranteed improvement over the existing, provided the new methodology provides a gain in efficiency over the existing.

This connected nicely with me reading several Nature articles on quantum computing during that trip,  where researchers from Google predict commercial products appearing in the coming five years, even though the technology is far from perfect and the outcome qubit error prone. Among the examples they provided, quantum simulation (not meaning what I consider to be simulation!), quantum optimisation (as a way to overcome multimodality), and quantum sampling (targeting given probability distributions). I find the inclusion of the latest puzzling in that simulation (in that sense) shows very little tolerance for errors, especially systematic bias. It may be that specific quantum architectures can be designed for specific probability distributions, just like some are already conceived for optimisation. (It may even be the case that quantum solutions are (just next to) available for intractable constants as in Ising or Potts models!)

Saône, sunrise #2 [jatp]

Posted in pictures, Running, Travel, University life with tags , , , , , , , , , , on April 10, 2017 by xi'an

Bayesian program synthesis

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , on April 7, 2017 by xi'an

Last week, I—along with Jean-Michel Marin—got an email from a journalist working for Science & Vie, a French sciences journal that published a few years ago a special issue on Bayes’ theorem. (With the insane title of “the formula that deciphers the World!”) The reason for this call was the preparation of a paper on Gamalon, a new AI company that relies on (Bayesian) probabilistic programming to devise predictive tools. And spent an hour skyping with him about Bayesian inference, probabilistic programming and machine-learning, at the general level since we had not heard previously of this company or of its central tool.

“the Gamalon BPS system learns from only a few examples, not millions. It can learn using a tablet processor, not hundreds of servers. It learns right away while we play with it, not over weeks or months. And it learns from just one person, not from thousands.”

Gamalon claims to do much better than deep learning at those tasks. Not that I have reasons to doubt that claim, quite the opposite, an obvious reason being that incorporating rules and probabilistic models in the predictor is going to help if these rule and models are even moderately realistic, another major one being that handling uncertainty and learning by Bayesian tools is usually a good idea (!), and yet another significant one being that David Blei is a member of their advisory committee. But it is hard to get a feeling for such claims when the only element in the open is the use of probabilistic programming, which is an advanced and efficient manner of conducting model building and updating and handling (posterior) distributions as objects, but which does not enjoy higher predictives abilities by default. Unless I live with a restricted definition of what probabilistic programming stands for! In any case, the video provided by Gamalon and the presentation given by its CEO do not help in my understanding of the principles behind this massive gain in efficiency. Which makes sense given that the company would not want to give up their edge on the competition.

Incidentally, the video in this presentation comparing the predictive abilities of the four major astronomical explanations of the solar system is great. If not particularly connected with the difference between deep learning and Bayesian probabilistic programming.

Statlearn17, Lyon

Posted in Kids, pictures, R, Statistics, Travel, University life with tags , , , , , , , , , , on April 6, 2017 by xi'an

Today and tomorrow, I am attending the Statlearn17 conference in Lyon, France. Which is a workshop with one-hour talks on statistics and machine learning. And which makes for the second workshop on machine learning in two weeks! Yesterday there were two tutorials in R, but I only took the train to Lyon this morning: it will be a pleasant opportunity to run tomorrow through a city I have not truly ever visited, if X’ed so many times driving to the Alps. Interestingly, the trip started in Paris with me sitting in the train next to another speaker at the conference, despite having switched seat and carriage with another passenger! Speaker whom I did not know beforehand and could only identify him by his running R codes at 300km/h.

machine learning-based approach to likelihood-free inference

Posted in Statistics with tags , , , , , , , , , , , on March 3, 2017 by xi'an

polyptych painting within the TransCanada Pipeline Pavilion, Banff Centre, Banff, March 21, 2012At ABC’ory last week, Kyle Cranmer gave an extended talk on estimating the likelihood ratio by classification tools. Connected with a 2015 arXival. The idea is that the likelihood ratio is invariant by a transform s(.) that is monotonic with the likelihood ratio itself. It took me a few minutes (after the talk) to understand what this meant. Because it is a transform that actually depends on the parameter values in the denominator and the numerator of the ratio. For instance the ratio itself is a proper transform in the sense that the likelihood ratio based on the distribution of the likelihood ratio under both parameter values is the same as the original likelihood ratio. Or the (naïve Bayes) probability version of the likelihood ratio. Which reminds me of the invariance in Fearnhead and Prangle (2012) of the Bayes estimate given x and of the Bayes estimate given the Bayes estimate. I also feel there is a connection with Geyer’s logistic regression estimate of normalising constants mentioned several times on the ‘Og. (The paper mentions in the conclusion the connection with this problem.)

Now, back to the paper (which I read the night after the talk to get a global perspective on the approach), the ratio is of course unknown and the implementation therein is to estimate it by a classification method. Estimating thus the probability for a given x to be from one versus the other distribution. Once this estimate is produced, its distributions under both values of the parameter can be estimated by density estimation, hence an estimated likelihood ratio be produced. With better prospects since this is a one-dimensional quantity. An objection to this derivation is that it intrinsically depends on the pair of parameters θ¹ and θ² used therein. Changing to another pair requires a new ratio, new simulations, and new density estimations. When moving to a continuous collection of parameter values, in a classical setting, the likelihood ratio involves two maxima, which can be formally represented in (3.3) as a maximum over a likelihood ratio based on the estimated densities of likelihood ratios, except that each evaluation of this ratio seems to require another simulation. (Which makes the comparison with ABC more complex than presented in the paper [p.18], since ABC major computational hurdle lies in the production of the reference table and to a lesser degree of the local regression, both items that can be recycled for any new dataset.) A smoothing step is then to include the pair of parameters θ¹ and θ² as further inputs of the classifier.  There still remains the computational burden of simulating enough values of s(x) towards estimating its density for every new value of θ¹ and θ². And while the projection from x to s(x) does effectively reduce the dimension of the problem to one, the method still aims at estimating with some degree of precision the density of x, so cannot escape the curse of dimensionality. The sleight of hand resides in the classification step, since it is equivalent to estimating the likelihood ratio. I thus fail to understand how and why a poor classifier can then lead to a good approximations of the likelihood ratio “obtained by calibrating s(x)” (p.16). Where calibrating means estimating the density.