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Wasserstein Lagrangian Flows explain many different dynamics on the space of distributions from a single perspective. arxiv.org/abs/2310.10649 I made a video explaining our (with Rob Brekelmans) #icml2024 paper about WLF. Like subscribe share, lol. youtu.be/kkddiLegc3s?si…

Come check out our presentations today! #ICML2024 Probabilistic Inference via Twisted Sequential Monte Carlo (Oral, 11:15-11:30, Hall A8) (Poster 814 morning) x.com/AliMakhzani/st… Wasserstein Lagrangian Flows (Poster 1408 afternoon) x.com/k_neklyudov/st…

Simulation-free training for scientific problems with aligned data (e.g. transition path sampling)! stripping away the science applications, we learn a distribution path to solve Schrödinger bridge w/nonlinear reference drift and δ-fn endpoints (i.e. no coupling optimization)

🧵(5/5) The main result that unlocks all these possibilities is our new Itô density estimator, an efficient way to estimate the density of the generated samples for an already-trained diffusion model (assuming that we know the score). It does not require any extra computations,

Mark Goldstein SMC methods are so back.

Got a diffusion model? What if there were a way to: - Get SOTA text-to-image prompt fidelity, with no extra training! - Steer continuous and discrete (e.g. text) diffusions - Beat larger models using less compute - Outperform fine-tuning - And keep your stats friends happy !?

🚀 New Rectified Flow materials (WIP)! 📖 Tutorials: rectifiedflow.github.io 💻 Code: github.com/lqiang67/recti… 📜 Notes: cs.utexas.edu/~lqiang/PDF/fl… Contributions from @RunlongLiao, Xixi Hu, Bo Liu, and many others! 🔥 Let us know your thoughts! 🚀

How annealing helps overcoming multimodality? In our ICLR 2025 paper openreview.net/forum?id=P6IVI… and preprint arxiv.org/abs/2502.04575, we established the first complexity bound for annealed sampling and normalizing constant (⇔free energy) estimation under weak assumptions on target!

We are excited to announce that registration for the inaugural post-Bayes workshop on May 15./16. at UCL is now open! Website: postbayes.github.io/workshop2025/ Registration link: tinyurl.com/postBayesWorks…

We are presenting 3 orals and 1 spotlight at #ICLR2025 on two primary topics: On generalizing the data-driven flow matching algorithm to jump processes, arbitrary discrete corruption processes, and beyond. And on highly scalable algorithms for reward-driven learning settings.

Given q_t, r_t as diffusion model(s), an SDE w/drift β ∇ log q_t + α ∇ log r_t doesn’t sample the sequence of geometric avg/product/tempered marginals! To correct this, we derive an SMC scheme via PDE perspective Resampling weights are ‘free’, depend only on (exact) scores!

Small plug, not really advertised but we similarly showed how to perform temperature based control and composition of separately trained diffusion models via SMC and the Feynman Kac model formalism, with score distillation of the energy at AISTATS last year - Diversity control