Paper

Factor Augmented High-Dimensional SGD

Unreadunread

AI summary

arXiv:2605. 19291v1 Announce Type: new Abstract: Stochastic gradient descent (SGD) is a fundamental optimization algorithm widely used in modern machine learning.

Reading status

Priority

About this paper

Authors: Shubo Li, Yuefeng Han, Xiufan Yu
Released: 2026-05-20 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Factor Augmented High-Dimensional SGD overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", 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 "Follow-up to #354: cascading chunk-binding — does A→B, B→C, C→D propagate the full chain on a recipient trained only to emit A?". Matching terms: under, line, rate, full, factor, stage. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.19291

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Factor Augmented High-Dimensional SGD. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.