A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper proves linear convergence of SCMS algorithm for directional data.
Theoretical guarantees for STE, a robust subspace recovery method.
Driven by a wide range of applications, many principal subspace estimation problems have been studied individually under different structural constraints. This paper presents a unified framework for the statistical analysis of a general structured principal subspace estimation problem which includes as special cases no…
Unified framework for nonconvex matrix completion with linearly parameterized factors.
Two new algorithms recover ridge lines from point clouds with convergence guarantees.
Estimates modes and ridges in mixed Euclidean and directional spaces.
New algorithm improves multitask learning across diverse agents.
Develops an MS-inspired algorithm for regression mode finding and space partitioning.
New method prevents forgetting in LLMs by dynamically identifying task-specific subspaces.