Archive for the Mountains Category

BAYSM 2020, Kunming, China [reposted]

Posted in Kids, Mountains, pictures, Statistics, Travel, University life with tags , , , , , , , , on January 13, 2020 by xi'an

The 5th Bayesian Young Statisticians Meeting, BAYSM2020, will take place in Kunming, China (June 26-27, 2020) as a satellite to the ISBA 2020 world meeting. BAYSM is the official conference of j-ISBA, the junior section of the International Society for Bayesian Analysis. It is intended for Ph.D. Students, M.S. Students, Post-Docs, Young and Junior researchers working in the field of Bayesian statistics, providing an opportunity to connect with the Bayesian community at large. Senior discussants will be present at each session, providing participants with hints, suggestions and comments to their work. Distinguished professors of the Bayesian community will also participate as keynote speakers, making an altogether exciting program.

Registration is now open (https://baysm2020.uconn.edu/registration) and will be available with an early bird discount until May 1, 2020. The event will be hosted at the Science Hall of Yunnan University (Kunming, China) right before ISBA 2020 world meeting. BAYSM 2020 will include social events, providing the opportunity to get to know other junior Bayesians.

Young researchers interested in giving a talk or presenting a poster are invited to submit an extended abstract by March 29, 2020. All the instructions for the abstract submission are reported at the page https://baysm2020.uconn.edu/call-dates

Thanks to the generous support of ISBA, a number of travel awards are available to support young researchers.

Keynote speakers:
Maria De Iorio
David Dunson
Sylvia Frühwirth-Schnatter
Xuanlong Nguyen
Amy Shi
Jessica Utts

Confirmed discussants:
Jingheng Cai
Li Ma
Fernando Quintana
Francesco Stingo
Anmin Tang
Yemao Xia

While the meeting is organized for and by junior Bayesians, attendance is open to anyone who may be interested. For more information, please visit the conference website: https://baysm2020.uconn.edu/

ABC in Grenoble, 19-20 March 2020 [registration open]

Posted in Mountains, Statistics, Travel, University life with tags , , , , , , , , , , , , , , on January 7, 2020 by xi'an

Reminding readers that the next occurrence of the “ABC in…” workshops will very soon take place in Grenoble, France, on 19-20 March 2020. Confirmed speakers and sessions (with more to come) are

Misspecified models

Links with Machine Learning

  • Flora Jay (Université d’Orsay, France) TBA
  • Pierre-Alexandre Mattei (Inria Sophia Antipolis – Méditerranée, France) Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation
  • Dennis Prangle (Newcastle University, UK) Scalable approximate inference for state space models with normalising flows

As in most earlier versions of the “ABC in…”workshops (ABC in Paris, London, Roma, &tc.), we are aiming at a workshop atmosphere and, thanks to local sponsors, the registration fees are null, but registration is compulsory. And now open!

I also remind ‘Og’s readers that Grenoble can be easily reached by fast trains from Paris, Roissy, Geneva and Lyon. (There are also flights to Grenoble airport from Warwick, as well as Bristol, Edinburgh, London, Manchester, Rotterdam, Stockholm, Warsaw, but this is less convenient than flying to Lyon Saint-Exupery airport and then catching a direct train at the airport.) To add to the appeal of the place, the workshop occurs during the skiing season, with three mountain ranges in the close vicinity. Making ABski a genuine possibility for the weekend after!

start of 2020

Posted in Kids, Mountains, pictures, Travel with tags , , , , , on January 1, 2020 by xi'an

emergence [jatp]

Posted in Mountains, pictures, Travel, University life with tags , , , , , , , on December 11, 2019 by xi'an

 

 

 

AABI9 tidbits [& misbits]

Posted in Books, Mountains, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , on December 10, 2019 by xi'an

Today’s Advances in Approximate Bayesian Inference symposium, organised by Thang Bui, Adji Bousso Dieng, Dawen Liang, Francisco Ruiz, and Cheng Zhang, took place in front of Vancouver Harbour (and the tentalising ski slope at the back) and saw more than 400 participants, drifting away from the earlier versions which had a stronger dose of ABC and much fewer participants. There were students’ talks in a fair proportion, as well (and a massive number of posters). As of below, I took some notes during some of the talks with no pretense at exhaustivity, objectivity or accuracy. (This is a blog post, remember?!) Overall I found the day exciting (to the point I did not suffer at all from the usal naps consecutive to very short nights!) and engaging, with a lot of notions and methods I had never heard about. (Which shows how much I know nothing!)

The first talk was by Michalis Titsias, Gradient-based Adaptive Markov Chain Monte Carlo (jointly with Petros Dellaportas) involving as its objective function the multiplication of the variance of the move and of the acceptance probability, with a proposed adaptive version merging gradients, variational Bayes, neurons, and two levels of calibration parameters. The method advocates using this construction in a burnin phase rather than continuously, hence does not require advanced Markov tools for convergence assessment. (I found myself less excited by adaptation than earlier, maybe because it seems like switching one convergence problem for another, with additional design choices to be made.)The second talk was by Jakub Swiatkowsk, The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks, involving mean field approximation in variational inference (loads of VI at this symposium!), meaning de facto searching for a MAP estimator, and reminding me of older factor analysis and other analyse de données projection methods, except it also involved neural networks (what else at NeurIPS?!)The third talk was by Michael Gutmann, Robust Optimisation Monte Carlo, (OMC) for implicit data generated models (Diggle & Graton, 1982), an ABC talk at last!, using a formalisation through the functional representation of the generative process and involving derivatives of the summary statistic against parameter, in that sense, with the (Bayesian) random nature of the parameter sample only induced by the (frequentist) randomness in the generative transform since a new parameter “realisation” is obtained there as the one providing minimal distance between data and pseudo-data, with no uncertainty or impact of the prior. The Jacobian of this summary transform (and once again a neural network is used to construct the summary) appears in the importance weight, leading to OMC being unstable, beyond failing to reproduce the variability expressed by the regular posterior or even the ABC posterior. It took me a while to wonder `where is Wally?!’ (the prior) as it only appears in the importance weight.

The fourth talk was by Sergey Levine, Reinforcement Learning, Optimal , Control, and Probabilistic Inference, back to Kullback-Leibler as the objective function, with linkage to optimal control (with distributions as actions?), plus again variational inference, producing an approximation in sequential settings. This sounded like a type of return of the MaxEnt prior, but the talk pace was so intense that I could not follow where the innovations stood.

The fifth talk was by Iuliia Molchanova, on Structured Semi-Implicit Variational Inference, from BAyesgroup.ru (I did not know of a Bayesian group in Russia!, as I was under the impression that Bayesian statistics were under-represented there, but apparently the situation is quite different in machine learning.) The talk brought an interesting concept of semi-implicit variational inference, exploiting some form of latent variables as far as I can understand, using mixtures of Gaussians.

The sixth talk was by Rianne van den Berg, Normalizing Flows for Discrete Data, and amounted to covering three papers also discussed in NeurIPS 2019 proper, which I found somewhat of a suboptimal approach to an invited talk, as it turned into a teaser for following talks or posters. But the teasers it contained were quite interesting as they covered normalising flows as integer valued controlled changes of variables using neural networks about which I had just became aware during the poster session, in connection with papers of Papamakarios et al., which I need to soon read.

The seventh talk was by Matthew Hoffman: Langevin Dynamics as Nonparametric Variational Inference, and sounded most interesting, both from title and later reports, as it was bridging Langevin with VI, but I alas missed it for being “stuck” in a tea-house ceremony that lasted much longer than expected. (More later on that side issue!)

After the second poster session (with a highly original proposal by Radford Neal towards creating  non-reversibility at the level of the uniform generator rather than later on), I thus only attended Emily Fox’s Stochastic Gradient MCMC for Sequential Data Sources, which superbly reviewed (in connection with a sequence of papers, including a recent one by Aicher et al.) error rate and convergence properties of stochastic gradient estimator methods there. Another paper I need to soon read!

The one before last speaker, Roman Novak, exposed a Python library about infinite neural networks, for which I had no direct connection (and talks I have always difficulties about libraries, even without a four hour sleep night) and the symposium concluded with a mild round-table. Mild because Frank Wood’s best efforts (and healthy skepticism about round tables!) to initiate controversies, we could not see much to bite from each other’s viewpoint.

off to Vancouver

Posted in Mountains, pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , , , , , on December 7, 2019 by xi'an

Today I am flying to Vancouver for an ABC workshop, the second Symposium on Advances in Approximate Bayesian Inference, which is a pre-NeurIPS workshop following five earlier editions, to some of which I took part. With an intense and exciting programme. Not attending the following NeurIPS as I had not submitted any paper (and was not considering relying on a lottery!). Instead, I will give a talk at ABC UBC on Monday 4pm, as, coincidence, coincidence!, I was independently invited by UBC to the IAM-PIMS Distinguished Colloquium series. Speaking on ABC on a broader scale than in the workshop. Where I will focus on ABC-Gibbs. (With alas no time for climbing, missing an opportunity for a winter attempt at The Stawamus Chief!)

where K. works

Posted in Books, Mountains, pictures, Statistics, Travel, University life with tags , , , , , , , , on December 2, 2019 by xi'an