First, we introduce FNOPE, a new simulation-based inference approach for efficiently and flexibly inferring function-valued parameters. By @gmoss13.bsky.social, @leahsmuhle.bsky.social, Reinhard Drews, @jakhmack.bsky.social and @coschroeder.bsky.social 2/11

FNOPE poster #601: Friday 5 Dec 4:30pm at San Diego and Thursday 4 Dec 10:30am at Copenhagen ➡️ openreview.net/forum?id=yB5... 3/11

Second, come by to check out NPE-PFN: We leverage the power of tabular foundation models for training-free and simulation-efficient SBI. SBI has never been so effortless! By @vetterj.bsky.social, Manuel Gloeckler, @danielged.bsky.social, @jakhmack.bsky.social 4/11

NPE-PFN poster #509: Thursday 4 Dec 11:00am at San Diego and Thursday 4 Dec 10:30am at Copenhagen ➡️ openreview.net/forum?id=kN0... 5/11

Third, in collaboration with @kyrakadhim.bsky.social, @philipp.hertie.ai, and others, we built a task- and data-constrained biophysical network of the outer plexiform layer of the mouse retina. To optimize this model, we built it on top of our Jaxley toolbox for differentiable simulation. 6/11

Retina model with Jaxley poster #2015: Friday 5 Dec 11:00am at San Diego ➡️ openreview.net/forum?id=ayj... 7/11

Fourth, in collaboration with Ian C Tanoh and Scott Linderman, we used the Jaxley toolbox and extended Kalman filters to estimate the marginal log-likelihood of a biophysical neuron model. We showed that this enables identifying biophysical parameters given extracellular recordings. 8/11

Kalman filtering meets Jaxley poster #2015: Thu 4 Dec 11:00am at San Diego ➡️ openreview.net/forum?id=1si... 9/11

Fifth, we bring AutoML to SBI pipelines with a practical performance metric that does not require ground-truth posteriors, improving inference quality on the SBI benchmark! By @swagatam.bsky.social, @gmoss13.bsky.social, @keggensperger.bsky.social, @jakhmack.bsky.social 10/11