Archive for AISTATS 2016
off to Sevilla [5thSYSORM]
Posted in Kids, pictures, Statistics, Travel, University life, Wines with tags 5thSYSORM, AISTATS 2016, Andalucía, Cadiz, conference, Guadalquivir, IMUS, Instituto de Matemáticas de la Universidad de Sevilla, Operational Research, Real Alcázar, SEIO, Sevilla, Spain, Young statisticians on November 5, 2025 by xi'an
2025 IMS International Conference on Statistics and Data Science in Sevilla [Dec. 15-18]
Posted in pictures, Statistics, Travel, University life with tags AISTATS 2016, Andalucía, Christopher Columbus, Christophorus Columbus, colonialism, Cristóbal Colón, Cristoforo Colombo, egg of Columbus, Guadalquivir, ICSDS 2024, ICSDS 2025, IMS, Institute of Mathematical Statistics, Nice, Sevilla, South America, Spanish Conquest on June 14, 2025 by xi'an
European statistics in Finland [EMS17]
Posted in Books, pictures, Running, Statistics, Travel, University life with tags ABC, AISTATS 2016, Amazon, AMIS, Bayesian optimisation, deterministic mixtures, EMS 2017, Europe, European Meeting of Statisticians, exact Monte Carlo, Helsinki, INLA, particle filters, probabilistic numerics, University of Helsinki on August 2, 2017 by xi'an
While this European meeting of statisticians had a wide range of talks and topics, I found it to be more low key than the previous one I attended in Budapest, maybe because there was hardly any talk there in applied probability. (But there were some sessions in mathematical statistics and Mark Girolami gave a great entry to differential geometry and MCMC, in the spirit of his 2010 discussion paper. Using our recent trip to Montréal as an example of geodesic!) In the Bayesian software session [organised by Aki Vetahri], Javier Gonzáles gave a very neat introduction to Bayesian optimisation: he showed how optimisation can be turned into Bayesian inference or more specifically as a Bayesian decision problem using a loss function related to the problem of interest. The point in following a Bayesian path [or probabilist numerics] is to reduce uncertainty by the medium of prior measures on functions, although resorting [as usual] to Gaussian processes whose arbitrariness I somehow dislike within the infinity of priors (aka stochastic processes) on functions! One of his strong arguments was that the approach includes the possibility for design in picking the next observation point (as done in some ABC papers of Michael Guttman and co-authors, incl. the following talk at EMS 2017) but again the devil may be in the implementation when looking at minimising an objective function… The notion of the myopia of optimisation techniques was another good point: only looking one step ahead in the future diminishes the returns of the optimisation and an alternative presented at AISTATS 2016 [that I do not remember seeing in Càdiz] goes against this myopia.
Umberto Piccini also gave a talk on exploiting synthetic likelihoods in a Bayesian fashion (in connection with the talk he gave last year at MCqMC 2016). I wondered at the use of INLA for this Gaussian representation, as well as at the impact of the parameterisation of the summary statistics. And the session organised by Jean-Michel involved Jimmy Olson, Murray Pollock (Warwick) and myself, with great talks from both other speakers, on PaRIS and PaRISian algorithms by Jimmy, and on a wide range of exact simulation methods of continuous time processes by Murray, both managing to convey the intuition behind their results and avoiding the massive mathematics at work there. By comparison, I must have been quite unclear during my talk since someone interrupted me about how Owen & Zhou (2000) justified their deterministic mixture importance sampling representation. And then left when I could not make sense of his questions [or because it was lunchtime already].
Florid’AISTATS
Posted in pictures, R, Statistics, Travel, University life with tags AISTATS 2016, AISTATS 2017, Annals of Statistics, Cadiz, Electronic Journal of Statistics, Florida, machine learning, NIPS 2017, proceedings, refereeing on August 31, 2016 by xi'anThe next AISTATS conference is taking place in Florida, Fort Lauderdale, on April 20-22. (The website keeps the same address one conference after another, which means all my links to the AISTATS 2016 conference in Cadiz are no longer valid. And that the above sunset from Florida is named… cadiz.jpg!) The deadline for paper submission is October 13 and there are two novel features:
- Fast-track for Electronic Journal of Statistics: Authors of a small number of accepted papers will be invited to submit an extended version for fast-track publication in a special issue of the Electronic Journal of Statistics (EJS) after the AISTATS decisions are out. Details on how to prepare such extended journal paper submission will be announced after the AISTATS decisions.
- Review-sharing with NIPS: Papers previously submitted to NIPS 2016 are required to declare their previous NIPS paper ID, and optionally supply a one-page letter of revision (similar to a revision letter to journal editors; anonymized) in supplemental materials. AISTATS reviewers will have access to the previous anonymous NIPS reviews. Other than this, all submissions will be treated equally.
I find both initiatives worth applauding and replicating in other machine-learning conferences. Particularly in regard with the recent debate we had at Annals of Statistics.
what to do with refereed conference proceedings?
Posted in Books, Statistics, University life with tags AISTATS 2016, Annals of Statistics, machine learning, NIPS 2015, proceedings, publication, refereeing on August 8, 2016 by xi'an
In the recent days, we have had a lively discussion among AEs of the Annals of Statistics, as to whether or not set up a policy regarding publications of documents that have already been published in a shortened (8 pages) version in a machine learning conference like NIPS. Or AISTATS. While I obviously cannot disclose details here, the debate is quite interesting and may bring the machine learning and statistics communities closer if resolved in a certain way. My own and personal opinion on that matter is that what matters most is what’s best for Annals of Statistics rather than the authors’ tenure or the different standards in the machine learning community. If the submitted paper is based on a brilliant and novel idea that can appeal to a sufficiently wide part of the readership and if the maths support of that idea is strong enough, we should publish the paper. Whether or not an eight-page preliminary version has been previously published in a conference proceeding like NIPS does not seem particularly relevant to me, as I find those short papers mostly unreadable and hence do not read them. Since Annals of Statistics runs an anti-plagiarism software that is most likely efficient, blatant cases of duplications could be avoided. Of course, this does not solve all issues and papers with similar contents can and will end up being published. However, this is also the case for statistics journals and statistics, in the sense that brilliant ideas sometimes end up being split between two or three major journals.
