AINewsnow

Generalization Bounds for Markov Algorithms through Entropy Flow Computations

This story is from 2026-09-02. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2502.07584v3 Announce Type: replace Abstract: Many learning algorithms can be represented as Markov processes, and understanding their generalization error is a central topic in learning theory. For specific continuous-time noisy algorithms, a prominent analysis technique relies on informatio…

Read the full story at arXiv stat.ML ↗

Timeline · 1 report

  1. 2026-09-02 04:00 · arXiv stat.ML
    Generalization Bounds for Markov Algorithms through Entropy Flow Computations

More stories

  1. Introducing Kimi K3 on Amazon Bedrock — AWS Machine Learning Blog
  2. Introducing Amazon SageMaker HyperPod Inference Gateway — AWS Machine Learning Blog
  3. Anthropic, OpenAI, SpaceXAI, Google sued over call to ‘pace’ AI development — Politico Technology
  4. Gemini Hacked Three Companies in First Known Breakout by Google’s AI — Wall Street Journal Technology
  5. Alibaba ships Qwen3.8-Omni-Flash to watch, listen and call tools — r/LocalLLM
  6. NVIDIA CEO Jensen Huang rejects ‘AI will end the world’ claim, yet cautions ‘we should go as fast as we can but...’ — Mint AI
  7. Meet the Data Agent in ChatGPT Work — OpenAI YouTube
  8. AI hallucination of Chinese nuclear components almost led to US military attack — Ars Technica AI

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