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David Debot @daviddebot.bsky.social · 03/09/2026

The recording of our tutorial on Probabilistic Concept Bottleneck Models at UAI 2026 is now online! Check it out here: Part 1: youtu.be/mi8fX21GxIE?... Part 2: youtu.be/0cu1R7nGlkk?... Presented by @giuseppemarra.bsky.social, Pietro Barbiero and me.

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David Debot @daviddebot.bsky.social · 06/07/2026

🚨 Can we trust that the “concepts” in concept-based models actually mean what we think they mean? Our new work, Prototype-Grounded Concept Models (PGCMs), makes concept alignment directly inspectable and correctable. #ICML26 Paper: arxiv.org/abs/2604.16076 🧵 (1/5)

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David Debot @daviddebot.bsky.social · 22/05/2026

Our accepted ICML paper on prototype-based Concept Bottleneck Models is now on arXiv! We introduce Prototype-Grounded Concept Models (PGCMs), enabling verifiable concept alignment through interpretable visual prototypes 🔍🧠. Check it out at: arxiv.org/abs/2604.16076

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Reposted by David Debot

lennertds.bsky.social @lennertds.bsky.social · 24/02/2026

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.

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Reposted by David Debot

Jaron Maene @jjcmoon.bsky.social · 05/12/2025

1/5 Tomorrow I’ll talk about the 𝐩𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐬𝐭𝐢𝐜 𝐩𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐬𝐞𝐦𝐚𝐧𝐭𝐢𝐜𝐬 𝐨𝐟 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐢𝐚𝐛𝐥𝐞 𝐩𝐫𝐨𝐯𝐢𝐧𝐠 at #NeurIPS San Diego (poster #614 11am). 📃 openreview.net/pdf?id=rEUbD... 📺 www.youtube.com/watch?v=sOTX...

openreview.net

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lennertds.bsky.social @lennertds.bsky.social · 14/05/2025

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!

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Reposted by David Debot

Jaron Maene @jjcmoon.bsky.social · 23/04/2025

We developed a library to make logical reasoning embarrasingly parallel on the GPU. For those at ICLR 🇸🇬: you can get the juicy details tomorrow (poster #414 at 15:00). Hope to see you there!

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Gabriele Venturato @gabventurato.bsky.social · 28/02/2025

If you're at #AAAI2025, come check out our demo on neurosymbolic reinforcement learning with probabilistic logic shields 🤖 Tomorrow (Sat, March 1) from 12:30–2:30 PM during the poster session 💻

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Reposted by David Debot

Jaron Maene @jjcmoon.bsky.social · 27/02/2025

We all know backpropagation can calculate gradients, but it can do much more than that! Come to my #AAAI2025 oral tomorrow (11:45, Room 119B) to learn more.

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Gabriele Venturato @gabventurato.bsky.social · 25/02/2025

🔥 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/... 🧵⬇️

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David Debot @daviddebot.bsky.social · 24/02/2025

🚀 Do you care about safe AI? Do you want RL agents that are both smart & trustworthy? At #AAAI2025, we present our demo for neurosymbolic RL—combining deep learning with probabilistic logic shields for safer, interpretable AI in complex environments. 🏰🔥 🧵👇 (1/8)

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David Debot @daviddebot.bsky.social · 04/12/2024

🚨 Interpretable AI often means sacrificing accuracy—but what if we could have both? Most interpretable AI models, like Concept Bottleneck Models, force us to trade accuracy for interpretability. But not anymore, due to Concept-Based Memory Reasoner (CMR)! #NeurIPS2024 (1/7)

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