A fantastic opportunity to join us in #Amsterdam for some hardcore #uncertainty in #AI 🦾🚲🌷✖️✖️✖️

#causality #tractable #probabilistic #models #neurosymbolic #imprecise #probabilities #statistical #methods and more

The Uncertainty in Artificial Intelligence conference (UAI) is on 🦋!

I'm a bit biased, but this is a fantastic conference for #probabilisticML, #causality, #causalML, #tractable #probabilistic #models, #imprecise probabilities,
#reasoning, #neurosymbolic approaches and more.

#causalSky #statSky

🚀 Excited to present our paper "Rethinking Probabilistic Circuit Parameter Learning" at AISTATS tomorrow!

What are the key bottlenecks in optimizing 𝒕𝒓𝒂𝒄𝒕𝒂𝒃𝒍𝒆 probabilistic models like Probabilistic Circuits? Can we design "Adam-equivalent" optimizers for these structures?

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!

#Nordic #probabilistic #AI starts in Trondheim 🇳🇴

I'll be here for a week TAing and giving a lecture on ⚡ #tractable⚡ models

and I'm in good company @andresmasegosa.bsky.social @vabor112.bsky.social @jesfrellsen.bsky.social and more covering all aspects of 🎲 AI

👉 nordic.probabilistic.ai#lecturers

Große Datenmengen erhöhen die Komplexität (Laufzeit).
Probabilistische Graphmodelle können die Inferenz effizient gestalten. Statt exakter Berechnung modellieren sie Wahrscheinlichkeiten und Unsicherheiten.
Die Trefferquote kann hoch sein, aber fast nie 100 %.

link.springer.com/article/10.1...

📣📣 Reminder 📣📣
🚨TPM 2026 submissions are due in 7 days!!🚨

Join our community by sharing your work in:

Tractable probabilistic models
Causality
Tensor networks
NeSy AI

We hope to see you in Amsterdam! @auai.org

tractable-probabilistic-modeling.github.io/tpm2026/

Probabilistic circuits are deep *tractable* probabilistic models that allow efficient and exact computation of marginals.

Traditionally, monotone circuits enforce non-negativity by using non-negative weights.

Paper: arxiv.org/abs/2408.00876

🧵 Preprint alert! Introducing Augmented Gaussian sum filters (AGSF), a novel class of Bayesian filtering algorithms which unifies Gaussian sum (GSF) and particle filters (PF) by interpolating continuously between them, while being robust to common failure modes. arxiv.org/abs/2605.21698

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/

👏I am doing work in my lab on how to enforce logical constraints in neural nets

👉 proceedings.neurips.cc/paper_files/...

👉 arxiv.org/abs/2409.13724

and we use complexity theory to trace the precise expressiveness of tractable generative models

👉 arxiv.org/abs/2408.11778

Joint work with Amit on using tractable probabilistic models to predict baryonic properties over dark matter halos and galaxies. At ICAART-2025. #cosmology

This study introduces globally constrained decoding (GCD) and probabilistic GCD, transforming sampling from large models. This method mitigates bias and ensures compliance with complex constraints, leading to faster convergence in structured outputs. https://arxiv.org/abs/2606.01926

This study presents probabilistic globally constrained decoding (P-GCD), enhancing large language models' accuracy for constraints like JSON schemas and SQL queries. P-GCD improves sampling convergence rates while also meeting constraints. https://arxiv.org/abs/2606.01926

A new method for text generation from large language models introduces Probabilistic Globally Constrained Decoding, enhancing sampling efficiency and adherence to requirements such as JSON schema and SQL validity, transforming AI-generated outputs. https://arxiv.org/abs/2606.01926

New methods enhance constrained outputs from large language models using globally and probabilistic GCD. These techniques improve convergence rates and ensure constraints are met, outperforming locally constrained decoding while optimizing GPU efficiency. https://arxiv.org/abs/2606.01926

Researchers developed a method for constrained text generation with large language models, enhancing reliability for SQL outputs. By utilizing tensorized finite automata and probabilistic modeling, their method ensures constraint adherence and boosts sampling. https://arxiv.org/abs/2606.01926

[2025-12-10] 📚 Updates in #TPM

(1) Tractable Probabilistic Models for Investment Planning

🔍 More at researchtrend.ai/communities/TPM

[2025-11-19] 📚 Updates in #TPM

(1) <a href="https://researchtrend.ai/papers/2511.13888" class="hover:underline text-blue-600 dark:text-sky-400 no-card-link" target="_blank" rel="noopener" data-link="bsky">Tractable Probabilistic Models for Investment Planning
(2) Tractable Probabilistic Models for Investment Planning
(3) <a href="https://researchtrend.ai/papers/2511.14001" class="hover:underline text-blue-600 dark:text-sky-400 no-card-link" target="_blank" rel="noopener" data-link="bsky">How to Marginalize in Causal Structure Learning?
(4) How to Marginalize in Causal Structure Learning?

🔍 More at researchtrend.ai/communities/TPM

[2025-10-28] 📚 Updates in #TPM

(1) On the Hardness of Approximating Distributions with Tractable Probabilistic Models

🔍 More at researchtrend.ai/communities/TPM

[2025-07-08] 📚 Updates in #TPM

(1) A Quantum Information Theoretic Approach to Tractable Probabilistic Models
(2) <a href="https://researchtrend.ai/papers/2507.04385" class="hover:underline text-blue-600 dark:text-sky-400 no-card-link" target="_blank" rel="noopener" data-link="bsky">Tractable Representation Learning with Probabilistic Circuits
(3) Tractable Representation Learning with Probabilistic Circuits

🔍 More at researchtrend.ai/communities/TPM

[2025-06-03] 📚 Updates in #TPM

(1) Neural Conditional Probability for Uncertainty Quantification
(2) <a href="https://researchtrend.ai/papers/2506.01824" class="hover:underline text-blue-600 dark:text-sky-400 no-card-link" target="_blank" rel="noopener" data-link="bsky">A Quantum Information Theoretic Approach to Tractable Probabilistic Models
(3) A Quantum Information Theoretic Approach to Tractable Probabilistic Models

🔍 More at researchtrend.ai/communities/TPM

[2025-03-18] 📚 Updates in #TPM

(1) <a href="https://researchtrend.ai/papers/2503.12162" class="hover:underline text-blue-600 dark:text-sky-400 no-card-link" target="_blank" rel="noopener" data-link="bsky">Probabilistic Graph Circuits: Deep Generative Models for Tractable Probabilistic Inference over Graphs
(2) Probabilistic Graph Circuits: Deep Generative Models for Tractable Probabilistic Inference over Graphs

🔍 More at researchtrend.ai/communities/TPM

🚀 Tractable Probabilistic Models are here!

Explore the latest research: researchtrend.ai/communities/... 📚

From sum-product networks to probabilistic circuits, these models enable efficient inference and scalable learning. Research peaked in the 2010s—what’s driving renewed interest? 🤔

Pedro Zuidberg Dos Martires: A Quantum Information Theoretic Approach to Tractable Probabilistic Models https://arxiv.org/abs/2506.01824 https://arxiv.org/pdf/2506.01824 https://arxiv.org/html/2506.01824