Researchers introduced TabPFN, a transformer model trained on 100 million synthetic datasets to predict unclassified or unregressed spreadsheet or database cells with no fine-tuning required. TabPFN outperformed top decision-tree methods like CatBoost and XGBoost on dozens of

It's a very exciting time in tech right now. If you're a first-rate programmer, there are a huge number of other places you can go work rather than at the company building the infrastructure of the police state.

I wrote this in part jokingly a few months ago but have now met US profs who've had research cancelled because of this very issue. The US has gone full on stupid at this point. Absolutely insane to give away research leadership for nothing.

I am so proud to co-organize the workshop on foundation models for structured data at ICML. At this workshop, we will discuss on how to further extend the GenAI revolution to tabular data, time series forecasting etc. If you work on this consider submitting your work by May 19!

Deadline coming up! Consider double submitting your Neurips submissions to our workshop for high quality reviews and discussions at the workshop.

In 2015, we ran a workshop on "Drawing causal inference from Big Data" at the NAS. Back then, “Big Data” felt like a buzzword. Ten years later, we might finally have a method to make it real.

finally, wind is changing its direction: causal inference becomes easier if we give up on designing a new estimation algorithm ourselves (i don't think we've evolved to do so ourselves well.) let learning find one for you!

Sounds like a pretty cool type of benchmark

I‘m at ICML this week. Happy to meet up and talk about anything tabular data, in-context learning and general deep learning :)