Robotics paper index
Thin-shell stability of Gaussian cooling: logconcave sampling with sesteric complexity from a cold start
One-line summary
A robotics research paper on Thin-shell stability of Gaussian cooling: logconcave sampling with sesteric complexity from a cold start.
Engineering notes
Engineering notes will be added by the Robot Papers editorial team.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为 VLA、具身智能、人形机器人控制、机器人操作等高价值论文补充中文说明。
Original abstract
We show that logconcave probability measures along the Gaussian cooling path have thin-shell stability, generalizing the thin-shell theorem. This result leads to improved complexity for the fundamental problem of sampling an arbitrary logconcave distribution from a cold start. For (near-)isotropic logconcave distributions, the complexity is nearly $n^{2.5}$, improving the previous bound of $n^{2.75}$, and matching the complexity of the abstract Speedy walk.
Links and sources
Need this topic turned into a technical roadmap?
Robot Papers can prepare a custom robotics literature review, code map, dataset map, and B2B technology assessment.
Request B2B research
Comments