Archive for INRIA

double descent

Posted in Books, Statistics, University life with tags , , , , , , , , , , , on November 7, 2019 by xi'an

Last Friday, I [and a few hundred others!] went to the SMILE (Statistical Machine Learning in Paris) seminar where Francis Bach was giving a talk. (With a pleasant ride from Dauphine along the Seine river.) Fancis was talking about the double descent phenomenon observed in recent papers by Belkin & al. (2018, 2019), and Mei & Montanari (2019). (As the seminar room at INRIA was quite crowded and as I was sitting X-legged on the floor close to the screen, I took a few slides from below!) The phenomenon is that the usual U curve warning about over-fitting and reproduced in most statistics and machine-learning courses can under the right circumstances be followed by a second decrease in the testing error when the number of features goes beyond the number of observations. This is rather puzzling and counter-intuitive, so I briefkly checked the 2019 [8 pages] article by Belkin & al., who are studying two examples, including a standard “large p small n” Gaussian regression. where the authors state that

“However, as p grows beyond n, the test risk again decreases, provided that the model is fit using a suitable inductive bias (e.g., least norm solution). “

One explanation [I found after checking the paper] is that the variates (features) in the regression are selected at random rather than in an optimal sequential order. Double descent is missing with interpolating and deterministic estimators. Hence requiring on principle all candidate variates to be included to achieve minimal averaged error. The infinite spike is when the number p of variate is near the number n of observations. (The expectation accounts as well for the randomisation in T. Randomisation that remains an unclear feature in this framework…)

postdoctoral position in computational statistical physics and machine learning

Posted in Statistics with tags , , , , , , , , on February 12, 2019 by xi'an

bâtiment Alan Turing [jatp]

Posted in pictures, Travel, University life with tags , , , , , on June 24, 2018 by xi'an

Bayesian workshop in the French Alps

Posted in Statistics with tags , , , , , , , on June 22, 2018 by xi'an

crowd-based peer review

Posted in Statistics with tags , , , , , , , , , , on June 20, 2017 by xi'an

In clear connection with my earlier post on Peer Community In… and my visit this week to Montpellier towards starting a Peer Community In Computational Statistics, I read a tribune in Nature (1 June, p.9) by the editor of Synlett, Benjamin List, describing an experiment conducted by this journal in chemical synthesis. The approach was to post (volunteered) submitted papers on a platform accessible to a list of 100 reviewers, nominated by the editorial board, who could anonymously comment on the papers and read others’ equally anonymous comments. With a 72 hours deadline! According to Benjamin List (and based on  a large dataset of … 10 papers!), the outcome of the experiment is one of better quality return than with traditional reviewing policies. While Peer Community In… does not work exactly this way, and does not aim at operating as a journal, it is exciting and encouraging to see such experiments unfold!

Michael Jordan’s seminar in Paris next week

Posted in Statistics, University life with tags , , , , , on June 3, 2016 by xi'an

Next week, on June 7, at 4pm, Michael will give a seminar at INRIA, rue du Charolais, Paris 12 (map). Here is the abstract:

A Variational Perspective on Accelerated Methods in Optimization

Accelerated gradient methods play a central role in optimization,achieving optimal rates in many settings. While many generalizations and extensions of Nesterov’s original acceleration method have been proposed,it is not yet clear what is the natural scope of the acceleration concept.In this paper, we study accelerated methods from a continuous-time perspective. We show that there is a Lagrangian functional that we call the Bregman Lagrangian which generates a large class of accelerated methods in continuous time, including (but not limited to) accelerated gradient descent, its non-Euclidean extension, and accelerated higher-order gradient methods. We show that the continuous-time limit of all of these methods correspond to travelling the same curve in space time at different speeds, and in this sense the continuous-time setting is the natural one for understanding acceleration.  Moreover, from this perspective, Nesterov’s technique and many of its generalizations can be viewed as a systematic way to go from the continuous-time curves generated by the Bregman Lagrangian to a family of discrete-time accelerated algorithms. [Joint work with Andre Wibisono and Ashia Wilson.]

(Interested readers need to register to attend the lecture.)

Bangalore workshop [ಬೆಂಗಳೂರು ಕಾರ್ಯಾಗಾರ] and new book

Posted in Books, pictures, R, Statistics, Travel, University life with tags , , , , , , , , , , , , on August 13, 2014 by xi'an

IIScOn the last day of the IFCAM workshop in Bangalore, Marc Lavielle from INRIA presented a talk on mixed effects where he illustrated his original computer language Monolix. And mentioned that his CRC Press book on Mixed Effects Models for the Population Approach was out! (Appropriately listed as out on a 14th of July on amazon!) He actually demonstrated the abilities of Monolix live and on diabets data provided by an earlier speaker from Kolkata, which was a perfect way to start initiating a collaboration! Nice cover (which is all I saw from the book at this stage!) that maybe will induce candidates to write a review for CHANCE. Estimation of those mixed effect models relies on stochastic EM algorithms developed by Marc Lavielle and Éric Moulines in the 90’s, as well as MCMC methods.