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.
Efficiently implements MEG for low-rank matrix optimization problems.
We study the structure of the set of algebraic curvature operators satisfying a sectional curvature bound under the light of the emerging field of Convex Algebraic Geometry. More precisely, we determine in which dimensions this convex semialgebraic set is a spectrahedron or a spectrahedral shadow; in particular, fo…
We consider the problem of learning a low-rank matrix, constrained to lie in a linear subspace, and introduce a novel factorization for modeling such matrices. A salient feature of the proposed factorization scheme is it decouples the low-rank and the structural constraints onto separate factors. We formulate the optim…
Study on likelihood functions, associative equations, and Frobenius manifolds.
Spectrahedral regression fits convex functions via a non-convex optimization problem.
Improved Bandit PCA with optimal regret bound.
A new sampling method for log-concave distributions with warm starts and barriers.