Researchers from ESSEC and ENSAE are organising a workshop at Institut Henri Poincaré, next June. This workshop will focus on exploring the latest advancements in Approximate Inference methods, with an emphasis on techniques such as Variational Inference and its related approaches. We aim to explore recent breakthroughs in computer science and statistics that enable inference across large-scale models and big datasets, surpassing conventional simulation-based methods. The workshop will bring together researchers from statistics, computer science, and econometrics to exchange ideas on cutting-edge methodological advances and their practical applications. Registration is open and program is available.
Archive for Institut Henri Poincaré
approximate inference in theory & practice, IHP, Paris, 10-11 June 2024
Posted in Statistics, University life with tags approximate inference, conference, ENSAE, ESSEC, IHP, Institut Henri Poincaré, Panthéon, simulation-based methods, variational inference, workshop on March 6, 2024 by xi'anrepulsive sampling
Posted in Books, Statistics, University life with tags Coulomb potential, determinantal point process, HMC, IHP, importance sampling, Institut Henri Poincaré, MCMC algorithms, Nikolai Bakhvalov, numerical integration, pinball sampler, quasi-Monte Carlo methods, repulsiveness, simulating copulas, slides, Sobol sequences, variance reduction on January 31, 2024 by xi'an
After a long absence from the monthly Séminaire Parisien de Statistique I attended one today at IHP, including a talk by Diala Hawat on repelled point processes for numerical integration by Hawat et al. The goal is to get (and prove) a universal variance improvement for numerical integration by applying a form of determinantal processes to initial simulations, as eg iid (Poisson process) sampling (without accounting for the O(N²) cost in moving these points). The repelled points are obtain by a single (why single?) move based on a force function (as shown in the slide below), inspired by a Coulomb potential (in the sense that said move appears as one gradient step along the potential). Which reminded me of the pinball sampler, even though the inverse norm was just there to create infinite repulsion near each point. A surprising feature of this repelling step is that it even modifies a (QMC) Sobol process with also an (empirical) improvement in the variance. I wonder if one could construct an MCMC algorithm that would target a joint distribution, maybe via a copula representation, maybe via an equivalent version of HMC.
As an aside, the Bakhvalov results on the existence of a worst case integrand for any deterministic or random sequence (see top slide) made me wonder what the shape of this worst case function is, esp. for a QMC sequence (eg, Sobol). And whether or not they are of any relevance as a counterfactor to the optimal importance functions.
inhomogeneous random systems [Institut Henri Poincaré, 23-24 Jan 2024]
Posted in Statistics, Travel, University life with tags IHP, inhomogeneous random systems, Institut Henri Poincaré, kinetic theory, KU Leuven, large deviation, LPSM, Paris, particle systems, Sorbonne Université, Université Paris Cité, workshop on January 15, 2024 by xi'an
sweet 60’s
Posted in Kids, pictures, Statistics, University life with tags Édouard-Léon Scott de Martinville, celebrations, CMB, cosmic microwave background, Eric Moulines, FIFA, football World Cup, group picture, IHP, Institut Henri Poincaré, Lawrence Berkeley National Laboratory, NeurIPS 2021, NYT, Paris, recording, The New York Times, Thomas Edison, Zeitschrift on October 9, 2023 by xi'an
The traditional group picture at the end of Eric Moulines’ 60th anniversary celebration, at IHP, Paris. Some of the participants had already left (and I am carefully hidding in the background). Among the celebrating talks reflecting the huge thematic diversity of EM’s carreer, Patrick Flandrin gave a great historical account of a certain Édouard-Léon Scott de Martinville and his invention of a sound recording device that did not meet with the same success as the later phonograph by Edison. A song he had registered in 1860 was retrieved in 2008 by a team of the Lawrence Berkeley National Laboratory, making it the earliest known intelligible voice recording in existence! Jean-François Cardoso explained how the team at Institut d’Astrophysique de Paris produced a near optimal estimate of the Cosmic Microwave Background (CMB) by linear projections preserving normality. Sara Filippi exposed a variational Bayes approach to selecting groups of variables in a GLM. Gareth Roberts illustrated retrospective sampling with his recent foray with Jeff Rosenthal in the lack of uniformity in the FIFA World Cup draws. Anatoli Iouditski spoke about a recent work on polyhedral estimation in statistical linear inverse problems. And Elisabeth Gassiat strolled through recent works on inference for hidden Markov models, including one at NeurIPS 2021 with Aapo Hyvärinen and others on nonlinear ICA. This was quite a fun meeting, with plenty of anecdotes and a few older pictures (even though I could not find any prior to 2005, which may have been the year I bought my first digital camera!)
Éric turns 60
Posted in Books, Mountains, Statistics, Travel, University life with tags Aussois, birthday, Dent Parrachée, ERC Synergy Grant, Eric Moulines, Festschrift, French Alps, generative models, IHP, Institut Henri Poincaré, Maurienne, Paris, Vanoise, variational inference on July 14, 2023 by xi'an |
Conférence en l’honneur d’Eric Moulines13-14 sept. 2023 IHP Paris (France)
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There will be a conference / Festschrift held for the 60th birthday of Eric Moulines [longtime friend, coauthor, ERC Synergy co-PI, whom I almost killed when missing a catch on Dent Parrachée!] at Institut Henri Poincaré, Paris, on 13 and 14 September 2023. Registration is free, but compulsory.
These two days will celebrate the variety of the research topics covered by Eric Moulines over thirty years and the broad and thorough impact he had on many different scientific communities. In particular, through more than 100 papers published in highly selective journals, he has made essential contributions in as many fields as statistical signal processing, time series analysis, inference in partially observed models, non-linear filtering, computational statistics, Markov Chain Monte Carlo, stochastic optimization methods, as well as hot topics related to artificial intelligence like generative models, variational inference or Bayesian machine learning.
