Archive for generative models
mostly MC [April]
Posted in Books, Kids, Statistics, University life with tags #ERCSyG, Bayesian computational methods, Bayesian inference, denoising, generative models, Institut PR[AI]RIE, JMLR, machine learning, Markov chain Monte Carlo, MCMC, Monte Carlo methods, Monte Carlo Statistical Methods, mostly Monte Carlo seminar, multimodal target, Ocean, optimization, Paris, PariSanté campus, PSC, sampling, score-based generative models, seminar, simulation, stochastic diffusions, stochastic localization, variational autoencoders on April 5, 2024 by xi'an
mostly M[ar]C[h]
Posted in Books, Kids, Statistics, University life with tags diffusions, generative modelling, generative models, Monte Carlo methods, Monte Carlo Statistical Methods, mostly Monte Carlo seminar, normalizing flow, optimal transport, optimization, Paris, PariSanté campus, push-forward distribution, seminar, stochastic diffusions, The Prairie Chair on February 27, 2024 by xi'an
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.
