Louis Grenioux
@theh2o64
PhD Student at École Polytechnique - Enhancing sampling with deep generative models
Thrilled to share that I successfully defended my PhD last Friday at École Polytechnique! It’s been an incredible journey with my advisor @marylougab and all the amazing researchers I met along the way. Next chapter: Research Fellow at @FlatironInst in New York, starting Jan 2026
🚨 Deadline Extension! 🚨 The FPI workshop submission deadline is now August 29th. Submit your latest research on Probabilistic Inference and Sampling. Don't miss out! fpineurips.framer.website #NeurIPS2025 #ProbabilisticInference #AI
📢 Deadline extended! Submit your work to the Frontiers in Probabilistic Inference workshop by Aug 29 🗓️ (fpineurips.framer.website). #NeurIPS2025 #MachineLearning #ProbabilisticInference
1/ Where do Probabilistic Models, Sampling, Deep Learning, and Natural Sciences meet? 🤔 The workshop we’re organizing at #NeurIPS2025! 📢 FPI@NeurIPS 2025: Frontiers in Probabilistic Inference – Learning meets Sampling Learn more and submit → fpiworkshop.org…
Working on Probabilistic Inference and Sampling 🤔? Consider submitting to the FPI workshop at #NeurIPS2025 this year. 🔗fpineurips.framer.website 🚨Deadline is coming up, and it's Aug 22nd! So submit your best work, early!
Excited to be at SampTA 2025 in Vienna this week! 🇦🇹 If you're around and interested in generative modeling or sampling, let's connect. More about the event: sampta25.univie.ac.at
1/ Where do Probabilistic Models, Sampling, Deep Learning, and Natural Sciences meet? 🤔 The workshop we’re organizing at #NeurIPS2025! 📢 FPI@NeurIPS 2025: Frontiers in Probabilistic Inference – Learning meets Sampling Learn more and submit → fpiworkshop.org…
We’re thrilled to announce the launch of the MolSS Reading Group! 🚀 🔬 MolSS = Machine Learning for Molecular Simulations and Sampling 🔥 First session: June 3rd, 3 PM (UK time) by @jmhernandez233 🗓️ Regular sessions: Every other Tuesday at 4 PM (UK time), 45–60 minutes
New revision of “BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching” 🚀 (1/6) We introduce NEM and BNEM — diffusion&energy-based neural samplers for Boltzmann distributions. 📝 Read our paper at [arxiv.org/pdf/2409.09787]
![TonyRKOuYang's tweet image. New revision of “BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching” 🚀
(1/6) We introduce NEM and BNEM — diffusion&energy-based neural samplers for Boltzmann distributions.
📝 Read our paper at [arxiv.org/pdf/2409.09787]](https://pbs.twimg.com/media/Gq2FK-cXkAA8Am7.jpg)
Big congrats to Charles Margossian and Lawrence Saul for winning the #AISTATS 2025 Best Paper Award! "Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrix"
Excited to present our work “Denoising Lévy Probabilistic Models” at #ICLR2025 (Thursday 10am) We extend diffusion models with heavy-tailed noise for better coverage & faster sampling. Let’s chat about heavy-tailed/discrete diffusion and novel methods! arxiv.org/abs/2407.18609
🔥Wanna talk about combining diffusion models and sampling ?🔥 I'll be attending ICLR next week to present my work (main track + FPI workshop) with the one and only @theh2o64, we'll be pleased to discuss about this hot topic. See you there !
I'll be at ICLR this week, hit me up if you want to discuss diffusion models and guidance! We'll be presenting arxiv.org/abs/2410.09945 on thu. 24 at the oral session 1C, then in the afternoon at Hall 2B Poster #143. Come say hi!
In other news, I'm visiting Prof. Hernández-Lobato (@jmhernandez233) at the engineering department of Cambridge's university 🇬🇧 till the end of June 2025. If you are in Cambridge or in the greater London area, I'd be happy to chat about sampling and generative models 😁
“Learned Reference-based Diffusion sampling” (LRDS) goes to #ICLR2025 ! 🚀 We propose to sample from multi-modal densities by turning a (reference) diffusion model, learnt using imperfect MCMC samples, into an accurate neural sampler. Joint work with @theh2o64 (co-lead),... ⏳
Nice to see interest in NF-MCMC! In line with my first paper (arxiv.org/abs/2302.04763), this work also compares different NF-MCMC algorithms with a big emphasis on NF architectures. If you want to dive in those algorithms, both papers give insightful intuitions 😁
David Nabergoj, Erik \v{S}trumbelj. [cs.LG]. Empirical evaluation of normalizing flows in Markov Chain Monte Carlo. arxiv.org/abs/2412.17136
📢 Super excited to announce the first ever Frontiers of Probabilistic Inference: Learning meets Sampling workshop at #ICLR2025 @iclr_conf! 🔗 website: sites.google.com/view/fpiworksh… We invite submissions on sampling, Bayesian inference, accelerating sampling in AI4Science,…
My two biggest regrets of not attending NeurIPS are missing @ArnaudDoucet1's keynote and @lipmanya's tutorial.
Live! Keynote talk by Arnaud Doucet From Diffusion Models to Schrödinger Bridges West Exhibition Hall C, B3 neurips.cc/virtual/2024/i…
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