Paper

Universal Multiclass Transductive Online Learning

Unreadunread

AI summary

arXiv:2605. 30479v1 Announce Type: new Abstract: We consider the problem of universal transductive online classification with a possibly unbounded label space.

Reading status

Priority

About this paper

Authors: Steve Hanneke, Hongao Wang
Released: 2026-06-01 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Universal Multiclass Transductive Online Learning overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", 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 "A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)". Matching terms: class, line, rate, without, trained. Source: arxiv cs.LG (Machine Learning).
arXiv: 2605.30479

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Universal Multiclass Transductive Online Learning. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.