AINewsnow

TabPFN and TabICL against tuned XGBoost: the model that does not train won on fourteen tables out of fourteen

The claim behind TabPFN and TabICL: they predict on a table without ever training on it, and still beat tuned boosting. Measured on 14 datasets from the Grinsztajn benchmark, same split and same clock for everyone. The model that does not train won on 14 out of 14 against tuned XGBoost. What it cos…

Read the full story at DEV Community — Machine Learning ↗

Timeline · 1 report

  1. 2026-09-28 01:01 · DEV Community — Machine Learning
    TabPFN and TabICL against tuned XGBoost: the model that does not train won on fourteen tables out of fourteen

More stories

  1. Scoop: Anthropic's Dario Amodei to have White House dinner with Trump — Axios AI+
  2. Bill Gates says unchecked AI could ‘cause a billion deaths’ in call for regulation — The Guardian AI
  3. Unsecured OpenAI agents posted 53 user images on the internet without the lab's knowledge — TechCrunch AI
  4. Scoop: Top AI companies probing tens of thousands of security incidents — Axios AI+
  5. OpenAI’s A.I. Went Rogue and Meddled With U.S. Government Websites — New York Times Technology
  6. The Surprising Reasons China Is Skeptical of A.I. Safety Calls — New York Times AI
  7. Did anyone do a full bench of e.g. Qwen Flash Next IQ4 and Qwen 27b FP8? Here are some — r/LocalLLaMA
  8. ‘Things Will Never Be Chill Again’: The Doomers Who Shaped the AI Safety Freakout — Wall Street Journal Technology

Get the daily brief of stories like this at 6:30 every morning →