AINewsnow

A Jump-Modified Wasserstein Gradient Flow for Maximum Mean Discrepancy with Energy Kernels

arXiv:2610.29007v1 Announce Type: cross Abstract: We study a jump-modified Wasserstein gradient flow of the squared maximum mean discrepancy generated by the energy kernels $K(z)=-|z|^q$, $0<q<2$, in every dimension $d\ge2$ as well as the one-dimensional range $1\le q<2$. Under subcritical density…

Read the full story at arXiv stat.ML ↗

Timeline · 1 report

  1. 2026-10-22 04:00 · arXiv stat.ML
    A Jump-Modified Wasserstein Gradient Flow for Maximum Mean Discrepancy with Energy Kernels

More stories

  1. OpenAI brings frontier models and Codex to Google Cloud — OpenAI News
  2. Recursive Releases Flint, an Open-Weight 40B MoE Local Coding Model for Agents with 2.5B Active Params — MarkTechPost
  3. Meta to create a new class of shares to fund AI push — The Verge AI
  4. NVIDIA Cosmos Opens New Tools to Train, Evaluate and Deploy World Models — NVIDIA Blog
  5. How Companies Are Using NVIDIA DSX to Break the Power Bottleneck — NVIDIA Blog
  6. Websites pulled offline worldwide as AI finds critical security flaws — South China Morning Post Tech
  7. Solving a 31-year-old grand challenge in mathematics – the KLS conjecture — Microsoft Research Blog
  8. OpenAI acquires Orchestra Bio to fold drug-development agents into Rosalind — OpenAI News

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