Archive for Québec

Poiré de glace [entre pierre & terre]

Posted in Travel, Wines with tags , , , , , , on January 20, 2021 by xi'an

non-uniform Laplace generation

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , on June 5, 2019 by xi'an

This year, the French Statistical Society (SFDS) Prix Laplace has been granted to Luc Devroye, author of the Non-Uniform Random Generation bible. among many achievements!, prize that he will receive during the 2019 meeting in Nancy, this very week.

the Montréal declarAIon

Posted in University life with tags , , , , , , , , , , , , on April 27, 2019 by xi'an

In conjunction with Yoshua Bengio being one of the three recipients of the 2018 Alan Turing award, Nature ran an interview of him about the Montréal Déclaration for a responsible AI, which he launched at NeurIPS last December.

“Self-regulation is not going to work. Do you think that voluntary taxation works? It doesn’t.”

Reflecting on the dangers of abuse of and by AIs, from surveillance, to discrimination, but being somewhat unclear on the means to implement the ten generic principles listed there. (I had missed the Declaration when it came up.) I agree with the principles stressed by this list, well-being, autonomy, privacy, democracy, diversity, prudence, responsability, and sustainability, it remains to be seem how they can be imposed upon corporations whose own public image puts more restraint on them than ethics or on governments that are all too ready to automatise justice, police, and the restriction of citizen’s rights. Which makes the construction of a responsible AI institution difficult to imagine, if the current lack of outreach of the extra-national institutions is the gauge. (A striking coincidence is that, when  Yoshua Bengio pointed out that France was trying to make Europe an AI power, there was also a tribune in Le Monde about the lack of practical impact of this call to arms, apart from more academics moving to half-time positions in private companies.) [Warning: the picture does not work well with the dark background theme of this blog.]

positions in North-East America

Posted in Kids, pictures, Statistics, Travel, University life with tags , , , , , , , , on September 14, 2017 by xi'an

Today I received emails about openings in both Université de Montréal, Canada, and Harvard University, USA:

  • Professor in Statistics, Biostatistics or Data Science at U de M, deadline October 30th, 2017, a requirement being proficiency in the French language;
  • Tenure-Track Professorship in Statistics at Harvard University, Department of Statistics, details there.

MCM17 snapshots

Posted in Kids, Mountains, pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , on July 5, 2017 by xi'an

At MCM2017 today, Radu Craiu presented a talk on adaptive Metropolis-within-Gibbs, using a family of proposals for each component of the target and weighting them by jumping distance. And managing the adaptation from the selection rate rather than from the acceptance rate as we did in population Monte Carlo. I find the approach quite interesting in that adaptation and calibration of Metropolis-within-Gibbs is quite challenging due to the conditioning, i.e., the optimality of one scale is dependent on the other components. Some of the graphs produced by Radu during the talk showed a form of local adaptivity that seemed promising. This raised a question I could not ask for lack of time, namely that with a large enough collection of proposals, it is unclear why this approach provides a gain compared with particle, sequential or population Monte Carlo algorithms. Indeed, when there are many parallel proposals, clouds of particles can be generated from all proposals in proportion to their appeal and merged together in an importance manner, leading to an easier adaptation. As it went, the notion of local scaling also reflected in Mylène Bédard’s talk on another Metropolis-within-Gibbs study of optimal rates. The other interesting sessions I attended were the ones on importance sampling with stochastic gradient optimisation, organised by Ingmar Schuster, and on sequential Monte Carlo, with a divide-and-conquer resolution through trees by Lindsten et al. I had missed.