L21 SNF compresses mixed-sign data robustly.
arXiv research
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For all complex dimensions n>=2, we construct complete Kaehler manifolds of bounded curvature and non-negative Ricci curvature whose Kaehler--Ricci evolutions immediately acquire Ricci curvature of mixed sign.
A new method using negative-shifted gradient descent improves overparameterized linear regression by avoiding structural limitations of negative ridge endpoints.
A new method improves graph-based semi-supervised classification by removing noise and mixed signs.
We obtain a vanishing theorem for the kernel of a Dirac operator on a Clifford module twisted by a sufficiently large power of a line bundle, whose curvature is non-degenerate at any point of the base manifold. In particular, if the base manifold is almost complex, we prove a vanishing theorem for the kernel of a $\spi…
We study the old problem of isometrically embedding a 2-dimensional Riemannian manifold into Euclidean 3-space. It is shown that if the Gaussian curvature vanishes to finite order and its zero set consists of two Lipschitz curves intersecting transversely at a point, then local sufficiently smooth isometric embeddings …
We define a generalization of Coxeter graphs and an associated Coxeter system and Coxeter mapping class. These can be used to construct periodic Coxeter mapping classes on surfaces with arbitrarily large genus, preserving lots of symmetries. The periodic mapping classes can in turn be used to construct sequences of pse…
Study long-only minimum variance portfolio in one-factor market with arbitrary sign betas.
Existence proved for complex 3-folds equation on a specific phase range.
Let be an oriented even-dimensional Riemannian manifold on which a discrete group of orientation-preserving isometries acts freely, so that the quotient is compact. We prove a vanishing theorem for a half-kernel of a -invariant Dirac operator on a -equivariant Clifford module over , twisted by …
A new algorithm solves signed Fréchet regression on manifolds with bounded curvature.
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A new family of multi-distribution divergences is characterized for fairness and other problems.