Archive for NIPS 2015
snapshot from Montréal
Posted in pictures, Running, Travel with tags Canada, conference, Montréal, NIPS 2015, Québec, René Levesque, snow on December 11, 2015 by xi'an
Je reviendrai à Montréal [D-2]
Posted in pictures, Statistics, Travel, University life with tags ABC, ABC in Montréal, Approximate Bayesian computation, Bayesian inference, Canada, London, MCMC, Monte Carlo integration, Monte Carlo Statistical Methods, Montréal, NIPS, NIPS 2015, probabilistic numerics, Robert Charlebois, scalability on December 9, 2015 by xi'an
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]
Posted in pictures, Statistics, Travel, University life with tags ABC, ABC in Montréal, Approximate Bayesian computation, Bayesian inference, Canada, MCMC, Monte Carlo integration, Monte Carlo Statistical Methods, Montréal, NIPS, NIPS 2015, probabilistic numerics, Québec, Robert Charlebois, scalability on September 30, 2015 by xi'anI 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
ABC à… Montréal
Posted in pictures, Statistics, Travel, University life with tags ABC, ABC in Montréal, Canada, CFE 2015, London, Montréal, NIPS 2014, NIPS 2015, probabilistic numerics, Québec on August 24, 2015 by xi'an
Like last year, NIPS will be hosted in Montréal, Québec, Canada, and like last year there will be an ACB NIPS workshop. With a wide variety of speakers and poster presenters. There will also be a probabilistic integration NIPS workshop, to which I have been invited to give a talk, following my blog on the topic! Workshops are on December 11 and 12, and I hope those two won’t overlap so that I can enjoy both at length (before flying back to London for CFE 2015…)
Update: they do overlap, both being on December 11…
arXiv frenzy
Posted in R, Statistics, University life with tags arXiv, Bayesian statistics, MCMC, Monte Carlo Statistical Methods, Montréal, NIPS 2015, particle filter on June 23, 2015 by xi'anIn the few past days, there has been so many arXiv postings of interest—presumably the NIPS submission effect!—that I cannot hope to cover them in the coming weeks! Hopefully, some will still come out on the ‘Og in a near future:
- arXiv:1506.06629: Scalable Approximations of Marginal Posteriors in Variable Selection by Willem van den Boom, Galen Reeves, David B. Dunson
- arXiv:1506.06285: The MCMC split sampler: A block Gibbs sampling scheme for latent Gaussian models by Óli Páll Geirsson, Birgir Hrafnkelsson, Daniel Simpson, Helgi Sigurðarson [also deserves a special mention for gathering only ***son authors!]
- arXiv:1506.06268: Bayesian Nonparametric Modeling of Higher Order Markov Chains by Abhra Sarkar, David B. Dunson
- arXiv:1506.06117: Convergence of Sequential Quasi-Monte Carlo Smoothing Algorithms by Mathieu Gerber, Nicolas Chopin
- arXiv:1506.06101: Robust Bayesian inference via coarsening by Jeffrey W. Miller, David B. Dunson
- arXiv:1506.05934: Expectation Particle Belief Propagation by Thibaut Lienart, Yee Whye Teh, Arnaud Doucet
- arXiv:1506.05860: Variational Gaussian Copula Inference by Shaobo Han, Xuejun Liao, David B. Dunson, Lawrence Carin
- arXiv:1506.05855: The Frequentist Information Criterion (FIC): The unification of information-based and frequentist inference by Colin H. LaMont, Paul A. Wiggins
- arXiv:1506.05757: Bayesian Inference for the Multivariate Extended-Skew Normal Distribution by Mathieu Gerber, Florian Pelgrin
- arXiv:1506.05741: Accelerated dimension-independent adaptive Metropolis by Yuxin Chen, David Keyes, Kody J.H. Law, Hatem Ltaief
- arXiv:1506.05269: Bayesian Survival Model based on Moment Characterization by Julyan Arbel, Antonio Lijoi, Bernardo Nipoti
- arXiv:1506.04778: Fast sampling with Gaussian scale-mixture priors in high-dimensional regression by Anirban Bhattacharya, Antik Chakraborty, Bani K. Mallick
- arXiv:1506.04416: Bayesian Dark Knowledge by Anoop Korattikara, Vivek Rathod, Kevin Murphy, Max Welling [a special mention for this title!]
- arXiv:1506.03693: Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference by Edward Meeds, Max Welling
- arXiv:1506.03074: Variational consensus Monte Carlo by Maxim Rabinovich, Elaine Angelino, Michael I. Jordan
- arXiv:1506.02564: Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families by Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone, Zoltan Szabo, Arthur Gretton [comments coming soon!]
