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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.

168,657 papers · 148 categories

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0111 · Dec 201119922001200920172026
6 results for p-densities

The paper defines and studies discrete p-density and compression-radius profiles of lattice knots.

problem Understanding geometric properties of lattice knots.
method Develops a framework for discrete p-density and compression-radius profiles of lattice knots, studying them on length-filtered sets and finite move-graph exploration.
result Density and compression-radius values are not monotone, illustrating distinct optimization problems.

The paper studies knot densities under various constraints and degenerations.

problem Understanding knot densities under different constraints and their degenerations.
method Introduces and analyzes unconstrained and ropelength-windowed pp-densities of knot types.
result The degenerations in the unconstrained theory and the introduction of ropelength-windowed densities.

Study on Hölder continuity of complex Monge-Ampère solutions on Stein spaces.

problem Understanding continuity of solutions to complex Monge-Ampère equations on Stein spaces.
method Analyzing solutions with LpL^p densities and Hölder boundary data on Stein spaces with isolated singularities.
result Solutions are Hölder continuous outside singular points if boundary data is Hölder continuous.

Let (X,ω)(X,ω) be a compact Kähler manifold. We obtain uniform Hölder regularity for solutions to the complex Monge-Ampère equation on XX with LpL^p right hand side, p>1p>1. The same regularity is furthermore proved on the ample locus in any big cohomology class. We also study the range $\MAH(X,ω)$ of the complex Monge-Am…

2011-12-06abs ↗pdf ↗

New findings on PAC learning and marginal distribution estimation.

problem Understanding how PAC learning relates to marginal distribution estimation under distributional constraints.
method Revisited the connection between PAC learning, uniform convergence, and density estimation, considering a known family of marginal distributions.
result PAC learning is sandwiched between two refined models of density estimation, differing only in whether the learner knows the set of well-estimated events in H.