AINewsnow

Functional Causal Discovery via Conditional Covariance Ordering

arXiv:2609.27256v1 Announce Type: cross Abstract: We study causal discovery where each node is a random function. Previous studies on this topic rely on structural assumptions, e.g., linearity or non-linearity, and distributional assumptions, e.g., Gaussianity or non-Gaussianity. In contrast, we ma…

Read the full story at arXiv stat.ML ↗

Timeline · 1 report

  1. 2026-09-24 04:00 · arXiv stat.ML
    Functional Causal Discovery via Conditional Covariance Ordering

More stories

  1. Introducing GPT-6 Sol and Luna — OpenAI News
  2. Introducing Gemini 3.8 Live with Live Avatar — Google Gemini Blog
  3. Gemini 3.8 text-to-speech says hello — Google Gemini Blog
  4. Sam Altman’s remarks at the United Nations Security Council — OpenAI News
  5. OpenAI Agent Hacked Australian Government Website — Wall Street Journal Technology
  6. Alibaba unveils new AI chip to challenge NVIDIA, plans Qwen models with up to 10 trillion parameters — Mint AI
  7. Introducing Ray-Ban Meta Audio and More AI Glasses Styles — Meta Newsroom
  8. Muse AI now hands over phone calls to human agents: Meta tests new feature in its personal assistant — Mint AI

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