Archive for Harvard University

Nature tidbits [the Bayesian brain]

Posted in Statistics with tags , , , , , , , , , , , , , , on March 8, 2020 by xi'an

In the latest Nature issue, a long cover of Asimov’s contributions to science and rationality. And a five page article on the dopamine reward in the brain seen as a probability distribution, seen as distributional reinforcement learning by researchers from DeepMind, UCL, and Harvard. Going as far as “testing” for this theory with a p-value of 0.008..! Which could be as well a signal of variability between neurons to dopamine rewards (with a p-value of 10⁻¹⁴, whatever that means). Another article about deep learning about protein (3D) structure prediction. And another one about learning neural networks via specially designed devices called memristors. And yet another one on West Africa population genetics based on four individuals from the Stone to Metal age (8000 and 3000 years ago), SNPs, PCA, and admixtures. With no ABC mentioned (I no longer have access to the journal, having missed renewal time for my subscription!). And the literal plague of a locust invasion in Eastern Africa. Making me wonder anew as to why proteins could not be recovered from the swarms of locust to partly compensate for the damages. (Locusts eat their bodyweight in food every day.) And the latest news from NeurIPS about diversity and inclusion. And ethics, as in checking for responsibility and societal consequences of research papers. Reviewing the maths of a submitted paper or the reproducibility of an experiment is already challenging at times, but evaluating the biases in massive proprietary datasets or the long-term societal impact of a classification algorithm may prove beyond the realistic.

unbiased MCMC with couplings [4pm, 26 Feb., Paris]

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , , , on February 24, 2020 by xi'an

On Wednesday, 26 February, Pierre Jacob (Havard U, currently visiting Paris-Dauphine) is giving a seminar on unbiased MCMC methods with couplings at AgroParisTech, bvd Claude Bernard, Paris 5ième, Room 32, at 4pm in the All about that Bayes seminar.

MCMC methods yield estimators that converge to integrals of interest in the limit of the number of iterations. This iterative asymptotic justification is not ideal; first, it stands at odds with current trends in computing hardware, with increasingly parallel architectures; secondly, the choice of “burn-in” or “warm-up” is arduous. This talk will describe recently proposed estimators that are unbiased for the expectations of interest while having a finite computing cost and a finite variance. They can thus be generated independently in parallel and averaged over. The method also provides practical upper bounds on the distance (e.g. total variation) between the marginal distribution of the chain at a finite step and its invariant distribution. The key idea is to generate “faithful” couplings of Markov chains, whereby pairs of chains coalesce after a random number of iterations. This talk will provide an overview of this line of research.

the Kouign-Amann experiment

Posted in Kids, pictures, Travel with tags , , , , , , , on June 10, 2019 by xi'an

Having found a recipe for Kouign-Amanns, these excessive cookies from Britanny that are essentially cooked salted butter!, I had a first try that ended up in disaster (including a deep cut on the remaining thumb) and a second try that went better as both food and body parts are concerned. (The name means cake of butter in Breton.)The underlying dough is pretty standard up to the moment it starts being profusedly buttered and layered, again and again, until it becomes sufficiently feuilleté to put in the oven. The buttery nature of the product, clearly visibly on the first picture, implies the cookies must be kept in containers like these muffin pans to preserve its shape and keep the boiling butter from  inundating the oven, two aspects I had not forecasted on the first attempt.The other if minor drawback of these cookies is that they do not keep well as they contain so much butter. Bringing enough calories input for an hearty breakfast (and reminding me of those I ate in Cambridge while last visiting Pierre).

position at Harvard

Posted in pictures, Running, University life with tags , , , , , , , , on October 27, 2018 by xi'an

This to point out an opening for a tenure track position in statistics and probability at Harvard University, with deadline December 1. More specifically, for a candidate in any field of statistics and probability as well as in any interdisciplinary areas where innovative and principled use of statistics and/or probability is of vital importance

controlled sequential Monte Carlo [BiPS seminar]

Posted in Statistics with tags , , , , , , , on June 5, 2018 by xi'an

The last BiPS seminar of the semester will be given by Jeremy Heng (Harvard) on Monday 11 June at 2pm, in room 3001, ENSAE, Paris-Saclay about his Controlled sequential Monte Carlo paper:

Sequential Monte Carlo methods, also known as particle methods, are a popular set of techniques to approximate high-dimensional probability distributions and their normalizing constants. They have found numerous applications in statistics and related fields as they can be applied to perform state estimation for non-linear non-Gaussian state space models and Bayesian inference for complex static models. Like many Monte Carlo sampling schemes, they rely on proposal distributions which have a crucial impact on their performance. We introduce here a class of controlled sequential Monte Carlo algorithms, where the proposal distributions are determined by approximating the solution to an associated optimal control problem using an iterative scheme. We provide theoretical analysis of our proposed methodology and demonstrate significant gains over state-of-the-art methods at a fixed computational complexity on a variety of applications.