Super excited that TabICLv2 is out 🎉
🚀Beats RealTabPFN-2.5 with no tuning and purely synthetic pre-training data.
👉Introduces QASSMax for long-context generalization, early target embedding, repeated feature grouping, Muon, etc., and a much diversified synthetic data prior.
🎉 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 @JingangQu, @DHolzmueller, @MarineLeMorvan, and myself 👇
🚨Visit Jingang at our ICML poster later today if you are interested in
- scaling tabular foundation models to larger datasets
- open-source tabular foundation models
- meta-learning + in-context learning
TabICL offers two checkpoints:
v1: Version in the paper, open-source pre-training code
v1.1: Better, preview of upcoming work, same architecture, benchmarked in TabArena. Pre-training code not yet public. Outperforms TabPFNv2 for, roughly, >= 7,000 samples.
Stay tuned for v2 😀
I’ll present "TabICL: A Tabular Foundation Model for In‑Context Learning on Large Data" at ICML 2025.
🗓 Tuesday, July 15, 2025 | 4:30–7:00 PM PDT
📍 East Exhibition Hall A‑B, Booth hashtag#E‑320
If you're curious about TabICL, come by the poster — I'd love to chat !
I’ll present "TabICL: A Tabular Foundation Model for In‑Context Learning on Large Data" at ICML 2025.
🗓 Tuesday, July 15, 2025 | 4:30–7:00 PM PDT
📍 East Exhibition Hall A‑B, Booth hashtag#E‑320
If you're curious about TabICL, come by the poster — I'd love to chat !
👨🎓🧾✨#icml2025 Paper: TabICL, A Tabular Foundation Model for In-Context Learning on Large Data
With @JingangQu, @DHolzmueller and @MarineLeMorvan
TL;DR: a well-designed architecture and pretraining gives best tabular learner, and more scalable
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