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Arik Reuter

@arikreuter.bsky.social
99 followers 172 following 28 posts

University of Cambridge and Max Planck Institute for Intelligent Systems I'm interested in amortized inference/PFNs/in-context learning for challenging probabilistic and causal problems. arikreuter.github.io

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Reposted by Arik Reuter
David Rügamer @davidruegamer.bsky.social · 24/05/2026
Our ICML'26 "Bayesian Two Cultures" Take on BNNs: 1/ Every 2nd paper: "MCMC for BNNs is infeasible. We thus propose [heuristic uncertainty method]." --- This is a misconception ❌
arxiv.org
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Arik Reuter @arikreuter.bsky.social · 08/05/2026
Last year, several “Causal Foundation Models” have been proposed—large, synthetically pre-trained transformers that estimate causal effects via in-context learning. [1-4] However, none of them can flexibly change its predictions based on the most critical assumption in causality: the causal graph.
arxiv.org
Use What You Know: Causal Foundation Models with Partial Graphs
Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions. Recently proposed Causal Foundation Models (CFMs) promise a more unified approach by amortisin...
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Reposted by Arik Reuter
Gaël Varoquaux @gaelvaroquaux.bsky.social · 12/02/2026
🎉 Announcing TabICLv2: State-of-the art Table Foundation Model, fast and open source A breakthrough for tabular ML: better prediction and faster runtime than alternatives, work by Jingang Qu, David Holzmüller @dholzmueller.bsky.social , Marine Le Morvan, and myself 👇
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Arik Reuter @arikreuter.bsky.social · 25/09/2025
Jake @jakemrobertson.bsky.social and I are super excited to share that our paper “Do-PFN: In-Context Learning for Causal Effect Estimation” has been accepted at NeurIPS as a spotlight! Check out our pre-print on arXiv and stay tuned for the updated version: arxiv.org/abs/2506.06039 [1/7]
arxiv.org
Do-PFN: In-Context Learning for Causal Effect Estimation
Estimation of causal effects is critical to a range of scientific disciplines. Existing methods for this task either require interventional data, knowledge about the ground truth causal graph, or rely...
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Machine Learning in Science @mackelab.bsky.social · 23/07/2025
New preprint: SBI with foundation models! Tired of training or tuning your inference network, or waiting for your simulations to finish? Our method NPE-PF can help: It provides training-free simulation-based inference, achieving competitive performance with orders of magnitude fewer simulations! ⚡️
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David Rügamer @davidruegamer.bsky.social · 16/07/2025
Can transformers learn full Bayesian inference in context? 🤔 👉 Come find out and visit our poster E-1205 in the East Hall this morning at #ICML2025 presented by @arikreuter.bsky.social icml.cc/virtual/2025...
icml.cc
ICML Poster Can Transformers Learn Full Bayesian Inference in Context?ICML 2025
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Reposted by Arik Reuter
Samuel Müller @sammuller.bsky.social · 08/07/2025
Compute is increasing much faster than data. How can we improve classical supervised learning long term (the underlying tech of most of GenAI)? Our ICML position paper's answer: simply train on a bunch of artificial data (noise) and only do inference on real-world data! 1/n
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Arik Reuter @arikreuter.bsky.social · 10/06/2025
We present a new approach to causal inference. Pre-trained on synthetic data, Do-PFN opens the door to a new domain: PFNs for causal inference—we are excited to announce our new paper “Do-PFN: In-Context Learning for Causal Effect Estimation” on Arxiv! 🔨🔍 A thread: 🧵[1/8]
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David Rügamer @davidruegamer.bsky.social · 01/05/2025
It seems that we have 3 accepted papers at ICML 2025 🔥
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David Rügamer @davidruegamer.bsky.social · 23/04/2025
Arriving in Singapore this afternoon 🛬 I'll attend #ICLR2025, #AABI2025, and #AISTATS2025 together with many of my students and collaborators to present our 2 orals, 5 posters, and 14 workshop contributions 🚀 Feel free to drop by!
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Reposted by Arik Reuter
Krassensteins @krassenstein.bsky.social · 23/03/2025
BREAKING: People are being suspended on X in Turkey for posting videos of these protests against Erdoğan’s corrupt and repressive regime. Keep sharing everywhere.
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