Archive for Nature Machine Intelligence

undecidable learnability

Posted in Books, Statistics, Travel, University life with tags , , , , , , on February 15, 2019 by xi'an

“There is an unknown probability distribution P over some finite subset of the interval [0,1]. We get to see m i.i.d. samples from P for m of our choice. We then need to find a finite subset of [0,1] whose P-measure is at least 2/3. The theorem says that the standard axioms of mathematics cannot be used to prove that we can solve this problem, nor can they be used to prove that we cannot solve this problem.”

In the first issue of the (controversial) nature machine intelligence journal, Ben-David et al. wrote a paper they present a s the machine learning equivalent to Gödel’s incompleteness theorem. The result is somewhat surprising from my layman perspective and it seems to only relate to a formal representation of statistical problems. Formal as in the Vapnik-Chervonenkis (PAC) theory. It sounds like, given a finite learning dataset, there are always features that cannot be learned if the size of the population grows to infinity, but this is hardly exciting…

The above quote actually makes me think of the Robbins-Wasserman counter-example for censored data and Bayesian tail prediction, but I am unsure the connection is anything more than sheer fantasy..!
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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).”