The first dynamic algorithms for matrix rank and related objects achieve update times scaling with rank r, specifically Õ(r^1.405) per entry update and Õ(r^1.528 + z) per column update, extending to dynamic maximum matching.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
Optimal reconstruction error from approximate linear queries converges to sqrt(2d/(d+1)) delta as number of queries T goes to infinity, with doubly exponential excess error decay for fixed d and exp(d) queries needed for vanishing excess when d grows.
The paper defines an intrinsic drift budget C_T in Fisher-Rao distance along the learner-environment trajectory and proves prequential reproducibility gaps bounded by order T^{-1/2} + C_T/T with a matching lower bound on regular subclasses.
citing papers explorer
-
Dynamic Rank, Basis, and Matching
The first dynamic algorithms for matrix rank and related objects achieve update times scaling with rank r, specifically Õ(r^1.405) per entry update and Õ(r^1.528 + z) per column update, extending to dynamic maximum matching.
-
Optimal Reconstruction from Linear Queries
Optimal reconstruction error from approximate linear queries converges to sqrt(2d/(d+1)) delta as number of queries T goes to infinity, with doubly exponential excess error decay for fixed d and exp(d) queries needed for vanishing excess when d grows.
-
Learning under Distributional Drift: Prequential Reproducibility as an Intrinsic Statistical Resource
The paper defines an intrinsic drift budget C_T in Fisher-Rao distance along the learner-environment trajectory and proves prequential reproducibility gaps bounded by order T^{-1/2} + C_T/T with a matching lower bound on regular subclasses.