Just a little under 5 days left to submit your work on #tractable#probabilistic#models to TPM 2026@auai.org (June 12, AoE)! If you have ongoing projects or recently accepted papers that are #tractable, #causal or #neurosymbolic, then check the CfP tractable-probabilistic-modeling.github.io/tpm2026/

If you care about enforcing constraints over time without breaking your computational resources, then read our new blog post over at @aihub.org! It focuses on showing how our neurosymbolic Markov models beat the SOTA in out-of-distribution generalisation and so much more.

AIhub.org@aihub.org · 6mo ago

aihub.org/2026/02/19/r...

Just under 10 days left to submit your latest endeavours in #tractable probabilistic models! Join us at TPM @auai.org #UAI2025 and show how to build #neurosymbolic / #probabilistic AI that is both fast and trustworthy!

antonio vergari ⚔️ short-circuiting@nolovedeeplearning.bsky.social · last yr.

the #TPM ⚡Tractable Probabilistic Modeling ⚡Workshop is back at @auai.org #UAI2025! Submit your works on: - fast and #reliable inference - #circuits and #tensor #networks - normalizing #flows - scaling #NeSy #AI ...& more! 🕓 deadline: 23/05/25 👉 tractable-probabilistic-modeling.github.io/tpm2025/

🚨 New paper: “Towards Adaptive Self-Normalized IS”, @ IEEE Statistical Signal Processing Workshop. TLDR; To estimate µ = E_p[f(θ)] with SNIS, instead of doing MCMC on p(θ) or learning a parametric q(θ), we try MCMC directly on p(θ)| f(θ)-µ | (variance-minimizing proposal). arxiv.org/abs/2505.00372

Towards Adaptive Self-Normalized Importance Samplers

The self-normalized importance sampling (SNIS) estimator is a Monte Carlo estimator widely used to approximate expectations in statistical signal processing and machine learning. The efficiency of S...

arxiv.org

Today we have @lennertds.bsky.social from KU Leuven teaching us how to adapt NeSy methods to deal with sequential problems 🚀 Super interesting topic combining DL + NeSy + HMMs! Keep an eye on Lennert's future works!

the #TPM ⚡Tractable Probabilistic Modeling ⚡Workshop is back at @auai.org #UAI2025! Submit your works on: - fast and #reliable inference - #circuits and #tensor #networks - normalizing #flows - scaling #NeSy #AI ...& more! 🕓 deadline: 23/05/25 👉 tractable-probabilistic-modeling.github.io/tpm2025/

🔥 Can AI reason over time while following logical rules in relational domains? We will present Relational Neurosymbolic Markov Models (NeSy-MMs) next week at #AAAI2025! 🎉 📜 Paper: arxiv.org/pdf/2412.13023 💻 Code: github.com/ML-KULeuven/... 🧵⬇️

Are you interested in more scalable reasoning under uncertainty and attending NeurIPS? Then pass by our poster #3708 later today at 4.30pm! 🕟 We use recursive integer arithmetic to express combinatorial problems and add uncertainty. Inference can be massively accelerated with tensors and the FFT. 🚀