MCM 2017

Je reviendrai à Montréal, as the song by Robert Charlebois goes, for the MCM 2017 meeting there, on July 3-7. I was invited to give a plenary talk by the organisers of the conference . Along with

Steffen Dereich, WWU Münster, Germany
Paul Dupuis, Brown University, Providence, USA
Mark Girolami, Imperial College London, UK
Emmanuel Gobet, École Polytechnique, Palaiseau, France
Aicke Hinrichs, Johannes Kepler University, Linz, Austria
Alexander Keller, NVIDIA Research, Germany
Gunther Leobacher, Johannes Kepler University, Linz, Austria
Art B. Owen, Stanford University, USA

Note that, while special sessions are already selected, including oneon Stochastic Gradient methods for Monte Carlo and Variational Inference, organised by Victor Elvira and Ingmar Schuster (my only contribution to this session being the suggestion they organise it!), proposals for contributed talks will be selected based on one-page abstracts, to be submitted by March 1.

Je reviendrai à Montréal [D-2]

I have spent the day and more completing and compiling slides for my contrapuntal perspective on probabilistic numerics, back in Montréal, for the NIPS 2015 workshop of December 11 on this theme. As I presume the kind  invitation by the organisers was connected with my somewhat critical posts on the topic, I mostly  The day after, while I am flying back to London for the CFE (Computational and Financial Econometrics) workshop, somewhat reluctantly as there will be another NIPS workshop that day on scalable Monte Carlo.

Je veux revoir le long désert
Des rues qui n’en finissent pas
Qui vont jusqu’au bout de l’hiver
Sans qu’il y ait trace de pas

Je reviendrai à Montréal [NIPS 2015]

I will be back in Montréal, as the song by Robert Charlebois goes, for the NIPS 2015 meeting there, more precisely for the workshops of December 11 and 12, 2015, on probabilistic numerics and ABC [à Montréal]. I was invited to give the first talk by the organisers of the NIPS workshop on probabilistic numerics, presumably to present a contrapuntal perspective on this mix of Bayesian inference with numerical issues, following my somewhat critical posts on the topic. And I also plan to attend some lectures in the (second) NIPS workshop on ABC methods. Which does not leave much free space for yet another workshop on Approximate Bayesian Inference! The day after, while I am flying back to London, there will be a workshop on scalable Monte Carlo. All workshops are calling for contributed papers to be presented during central poster sessions. To be submitted to abcinmontreal@gmail.com and to probnum@gmail.com and to aabi2015. Before October 16.

Funny enough, I got a joking email from Brad, bemoaning my traitorous participation to the workshop on probabilistic numerics because of its “anti-MCMC” agenda, reflected in the summary:

“Integration is the central numerical operation required for Bayesian machine learning (in the form of marginalization and conditioning). Sampling algorithms still abound in this area, although it has long been known that Monte Carlo methods are fundamentally sub-optimal. The challenges for the development of better performing integration methods are mostly algorithmic. Moreover, recent algorithms have begun to outperform MCMC and its siblings, in wall-clock time, on realistic problems from machine learning.

The workshop will review the existing, by now quite strong, theoretical case against the use of random numbers for integration, discuss recent algorithmic developments, relationships between conceptual approaches, and highlight central research challenges going forward.”

Position that I hope to water down in my talk! In any case,

Je veux revoir le long désert
Des rues qui n’en finissent pas
Qui vont jusqu’au bout de l’hiver
Sans qu’il y ait trace de pas