Moritz Willig

Not at #ICML2025 myself, but Florian & Roshni are presenting our spotlight poster on continual confounding + our respective ConCon dataset on Thursday 17 Jul 4:30 pm - 7 pm PDT in East Exhibition Hall A-B: E-3309 icml.cc/virtual/2025/p… Please talk to them! :) Details below ⬇️

Hesse plans major cuts to university funding. This article, featuring my colleague Stefan Roth (TU Darmstadt), highlights the serious consequences. It risks widening the gap with states like Bavaria & Baden-Württemberg—and with leading high-tech nations: bit.ly/44prCWO

🧠Foundation models are powerful—but what happens when they remember too much? Join us at #ICML2025 for our workshop on “The Impact of Memorization on Trustworthy Foundation Models” 👉icml2025memfm.github.io Let’s talk about memorization & what it takes to build trustworthy AI!

Thrilled to share our #ICLR2025 work on Meta-Causal States! 🌟 Causal graphs evolve with dynamic systems & agent actions. We show how to cluster causal models by qualitative behavior, revealing hidden dynamics & emergent relationships 🚀 #Causality #ML arxiv.org/abs/2410.13054

Three days left to apply for a PhD in my group! ⬇️⬇️ Come join us to work on the forefront of adaptive AI & lifelong machine learning. We’ve got lots of exciting ideas in store 🤗

Don't miss this! Also remember, that we have an associated Continual Confounding Challenge running: kaggle.com/competitions/c… . Early challenge deadline is today AoE, but submissions will be open until 7. January. Selected papers will be given the chance to present at the bridge.

🔥More Continual Learning meets Causality 🔥 In addition to our call for papers (by Nov 25), our Continual Causality Bridge at AAAI-25 has now opened the first continual confounding challenge! Submit challenge solutions by Jan 7 on Kaggle! More info: continualcausality.org/challenge/

The first “ConCon” challenge, tackling continual confounding, has just been launched!💡 Submit 3-page reports (non-archival) as part of the third Cont. Causal. Bridge at AAAI@2025. (Early deadline is Dec 6, 2024 AoE) Check out the full announcement here: continualcausality.org/challenge/

Our Continual Causality Bridge is going into its 3rd edition at AAAI-25 @RealAAAI ! 🎉 Join us for a spectacular 2 days of continual learning & causality by submitting your original 4 page work (for proceedings or non-archival tracks) until Nov 25 AOE! continualcausality.org/cfp/

Late announcement due to JMLR reviews and AC duties for NeurIPS. @FlorianPBusch will present our work on scaling causal inference to large scale real world data sets using probabilistic circuits at PGM 2024. Very excited for this line of research. Paper: raw.githubusercontent.com/mlresearch/v24…

In prior work, we argued that LLMs recite causal knowledge embedded in the data. At #AIME2024, we now show how to combine this with structure learning and dedicated new data to induce medical models! Causal Parrots 👉 openreview.net/forum?id=tv46t… AIME 👉 link.springer.com/chapter/10.100…

Great talk @toniwuest . If your interested in learning more about visual concepts via program synthesis check out our poster @UncertaintyInAI tomorrow at 2 PM. paper: arxiv.org/pdf/2402.08280 #UAI2024

Heading to #ICLR2024? Interested in causal reasoning? Check out our latest findings on a new causal bias (chains vs. common causes) in humans and LLMs. We show how model temperature affects these biases using earth mover's distance. Join the discussion at the Re-Align Workshop!

The unreliable "causal mind" of LLMs. We show that causal judgments of LLMs are biased in a very human-like fashion by examining two structures: Canonical Chain (A→B→C) & Common Cause (A←B→C). openreview.net/forum?id=544P6… Re-Align@ICLR2024 representational-alignment.github.io

Really excited to present our #Neurips2023 paper “Do Not Marginalize Mechanisms, Rather Consolidate!” (arxiv.org/abs/2310.08377), tomorrow (Thu) during poster session 6, poster #1012. Feel free to stop by!

🥳Thirteen (13!) @NeurIPSConf 2023 papers! Super proud of our Cluster of Excellence initiative RAI — Reasonable #AI 🚀 Learning+Reasoning. Composable. Human-Like. Open. Decentgralized. Together

LLM's might have capabilities but causality isn't one of them!! This is one of my favorite papers and I am happy it is now published in @TmlrOrg. We show how LLM's are "causal parrots" although they may seem causal at face value. With @matej_zecevic @MoritzWilligAI @kerstingAIML

Causal Parrots: Large Language Models May Talk Causality But Are Not Causal Matej Zečević, Moritz Willig, Devendra Singh Dhami, Kristian Kersting. Action editor: Frederic Sala. openreview.net/forum?id=tv46t… #causal #ai #inference