Werner Zellinger

I am very happy to share the first paper of my PhD: SIMSHIFT - A Benchmark for Adapting Neural Surrogates to Distribution Shifts 1/n

We introduce SIMSHIFT: A Benchmark for Adapting Neural Surrogates to Distribution Shifts. I sincerely hope that new ideas are coming out from this benchmark. Paper: arxiv.org/abs/2506.12007 Code: github.com/psetinek/simsh…

I will be speaking about our recent series of papers on learning in infinite dimensions! Joint Seminar on Inverse Problems and Learning Theory on Oct 29, 14:00 CET (online). Seminar homepage: sites.google.com/view/joint-sem…

📢 I would like to draw your attention to our seminar on ** Inverse Problems and Learning Theory ** sites.google.com/view/joint-sem…

The Joint Seminar on Inverse Problems and Learning Theory is a presentation-based online space for discussing research at the intersection of: Inverse Problems, Learning Theory, Statistics, Information Theory and Machine Learning. Everyone interested is welcome!

🚀 SymbolicAI – a framework for logic-based approaches combining generative models and solvers. Alongside, we introduce a benchmark and empirical measure to evaluate SOTA LLMs in AI-centric workflows. Read more in our paper arxiv.org/abs/2402.00854 #MachineLearning 🧠💡[1/n]

OPEN POSITIONS! We have open positions for PhD students and PostDocs at my lab both in core machine learning as well as in "Machine Learning in Life Sciences": jku.at/en/lit-artific…

For people @iclr_conf, Clifford Neural Layers for PDE Modeling air today at #130 from 11:30 onwards. Happy to chat about Clifford or geometric algebras, (large-scale) PDE modeling, or anything related! microsoft.github.io/cliffordlayers/

Meet us at 15:00 for our oral presentation at ICLR in room AD12. arxiv.org/abs/2305.01281

Our paper "Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation" has been selected for oral presentation (notable-top-5%) at #ICLR2023 openreview.net/forum?id=M95oD…. [1/n]