Paper

Riemannian Stochastic Optimization for Sufficient Dimension Reduction

Unreadunread

AI summary

arXiv:2606. 00413v1 Announce Type: new Abstract: Sufficient dimension reduction (SDR) makes high-dimensional regression tractable by projecting the covariates onto a low-dimensional subspace that preserves the conditional mean of the response.

Reading status

Priority

About this paper

Authors: Thibault Pautrel, Fran\c{c}ois Portier
Released: 2026-06-02 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Riemannian Stochastic Optimization for Sufficient Dimension Reduction overlaps with clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe", experiment "#351 follow-up: broader-vocab position-0 sweep at T=1.0 + position-1 suffix isolation". Matching terms: rate, project. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.00413

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Riemannian Stochastic Optimization for Sufficient Dimension Reduction. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.