In a solvable attention model, pre-training followed by rank-one LoRA admits sharp asymptotic predictions for test errors and representation alignment via an effective noise term.
Topological trivialization in non-convex empirical risk minimization, 2026
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Characterizes training error and test-training relation for an IAMP algorithm in multi-index ERM under high-d asymptotics, expecting optimality among polynomial-time methods based on prior related models.
citing papers explorer
-
High-Dimensional Theory of LoRA Fine-Tuning in a Solvable Attention Model
In a solvable attention model, pre-training followed by rank-one LoRA admits sharp asymptotic predictions for test errors and representation alignment via an effective noise term.
-
Replica Symmetry Breaking and Algorithmic Thresholds in Empirical Risk Minimization under Multi-Index Model
Characterizes training error and test-training relation for an IAMP algorithm in multi-index ERM under high-d asymptotics, expecting optimality among polynomial-time methods based on prior related models.