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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.

168,738 papers · 148 categories

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48 results for Cuts

NeuralCut learns to select cutting planes by looking ahead, outperforming traditional methods.

problem Selecting effective cutting planes for MILP optimization.
method Imitation learning on a lookahead expert to train a neural network for cut selection.
result NeuralCut outperforms standard baselines in cut selection for MILP benchmarks.

Differentiable cutting-plane layers solve parametric mixed-integer linear optimization problems.

problem Solving parametric mixed-integer linear optimization problems with changing data.
method Introducing cutting-plane layers (CPLs) for differentiable cutting-plane generation.
result The algorithm computes solutions with low integrality gaps and generalizes to unseen instances.

NeVI-Cut uses neural networks to efficiently propagate uncertainty without feedback.

problem Efficiently propagating uncertainty in downstream Bayesian analysis without feedback.
method NeVI-Cut combines neural networks and normalizing flows for variational inference.
result NeVI-Cut achieves significant computational gains and higher accuracy than traditional methods.

Paper connects probability density cuts to graph theory eigenfunctions.

problem Developing sparse cuts for probability densities.
method Defines sparse cuts and principal eigenfunctions for probability densities, proving Cheeger and Buser inequalities.
result No such inequalities hold for prior definitions, proving new inequalities for probability densities.

Stability of cut locus under metric perturbations in compact Riemannian manifolds.

problem Stability of cut locus under C2C^2-perturbations of the metric.
method Proving stability with respect to the Hausdorff metric of the cut locus under C2C^2 perturbation of the metric.
result The Hausdorff distance between cut loci converges to zero as the metrics converge.

Study shows convergence rates for Cheeger cuts on data clouds.

problem Optimizing graph cuts for clustering data sampled from a manifold.
method Analyzes statistical properties of Cheeger cuts on proximity graphs built from data.
result Obtains high probability convergence rates for Cheeger constant and cuts.

Unified framework for differentiable graph partitioning with probabilistic cuts.

problem Lack of general guarantees and principled gradients in prior probabilistic relaxations of graph cuts.
method Unified probabilistic framework covering a wide class of cuts, including Normalized Cut, with tight analytic upper bounds.
result Rigorous, numerically stable foundation for scalable, differentiable graph partitioning.

We define spin-c prequantization of a symplectic manifold to be a spin-c structure and a connection which are compatible with the symplectic form. We describe the cutting of an S^1-equivariant spin-c prequantization. The cutting process involves a choice of a spin-c prequantization for the complex plane. We prove that …

2007-10-23abs ↗pdf ↗

A symplectic cut of a manifold M with a Hamiltonian circle action is a symplectic quotient of M x C. If M is Kaehler then, since C is Kaehler, the cut space is Kaehler as well. The symplectic structure on the cut is well understood. In this paper we describe the complex structure (and hence the metric) on the cut. We t…

2002-12-04abs ↗pdf ↗

In this note, we study the cut locus of the free, step two Carnot groups Gk\mathbb{G}_k with kk generators, equipped with their left-invariant Carnot-Carathéodory metric. In particular, we disprove the conjectures on the shape of the cut loci proposed in [Myasnichenko - 2002] and [Montanari, Morbidelli - 2016], by exh…

2016-10-05abs ↗pdf ↗

Spectral Clustering as a relaxation of the normalized/ratio cut has become one of the standard graph-based clustering methods. Existing methods for the computation of multiple clusters, corresponding to a balanced kk-cut of the graph, are either based on greedy techniques or heuristics which have weak connection to th…

2015-05-24abs ↗pdf ↗

Spectral clustering is sensitive to how graphs are constructed from data particularly when proximal and imbalanced clusters are present. We show that Ratio-Cut (RCut) or normalized cut (NCut) objectives are not tailored to imbalanced data since they tend to emphasize cut sizes over cut values. We propose a graph partit…

2013-09-09abs ↗pdf ↗

This paper establishes the consistency of a family of graph-cut-based algorithms for clustering of data clouds. We consider point clouds obtained as samples of a ground-truth measure. We investigate approaches to clustering based on minimizing objective functionals defined on proximity graphs of the given sample. Our f…

2014-11-24abs ↗pdf ↗

In this article we extend cutting and blowing up to the nonrational symplectic toric setting. This entails the possibility of cutting and blowing up for symplectic toric manifolds and orbifolds in nonrational directions.

2016-06-02abs ↗pdf ↗

Spectral clustering methods which are frequently used in clustering and community detection applications are sensitive to the specific graph constructions particularly when imbalanced clusters are present. We show that ratio cut (RCut) or normalized cut (NCut) objectives are not tailored to imbalanced cluster sizes sin…

2016-08-26abs ↗pdf ↗

Functor connects symplectic and contact structures via cutting and blowups.

problem Establishing a functorial relationship between symplectic and contact structures.
method Developed a cutting procedure and its inverse for manifolds with boundary and equivariant transverse maps, then applied it to non-symplectic and non-contact structures.
result Obtained an inverse functor for equivariant radial-squared blowups.

We show how to associate an R-tree to the set of cut points of a continuum. If X is a continuum without cut points we show how to associate an R-tree to the set of cut pairs of X.

2009-05-15abs ↗pdf ↗

A new spectrum recovers cobordism cut and paste groups of manifolds with boundary.

problem Defining and studying cobordism cut and paste groups of manifolds with boundary.
method Constructing a spectrum that recovers the cobordism cut and paste groups of manifolds with boundary.
result Construction of a spectrum that recovers the cobordism cut and paste groups of manifolds with boundary.

The paper extends spacetime topology results using codimension 2 null cut locus properties.

problem Understanding spacetime topology with and without horizons.
method Review and extension of existing literature on spacetime topology, utilizing codimension 2 null cut locus properties.
result Results for spacetimes with and without horizons, including asymptotically AdS settings.

The paper explores new phenomena in boundaries of relatively hyperbolic groups.

problem Exploring new phenomena in boundaries of relatively hyperbolic groups.
method Combination theorem to create examples of relatively hyperbolic groups with parabolic cut pairs.
result All relatively hyperbolic groups with inseparable parabolic cut pairs arise via the combination theorem.

Generalizes neural network verification by adding arbitrary cutting planes.

problem Handling general cutting plane constraints in neural network verification.
method Generalized bound propagation method (GCP-CROWN) that allows arbitrary cutting plane constraints.
result GCP-CROWN significantly improves neural network verification performance.

We showed in another paper [arXiv:1103.1759] that every connected graph can be realized as the cut locus of some point on some riemannian surface SS. Here, criteria for the orientability of SS are given, and are applied to classify the distinct, orientable, cut locus structures on graphs with four generating cycles.

2011-03-16abs ↗pdf ↗

This paper shows feature importance remains valid even in low-performing models.

problem Feature importance validity in low-performing machine learning models for biomedical data.
method Experiments with synthetic and real biomedical datasets to compare feature rank stability under different data reductions.
result Feature importance can be maintained even at low performance levels if data size is adequate.

We propose a new method to model multi-way similarities into hypergraphs for clustering.

problem Clustering real-valued data using hypergraphs with multi-way similarities.
method Formulate multi-way similarities using kernel functions, establish connections to hypergraph cut, and develop a fast spectral clustering algorithm.
result Our method outperforms existing graph and heuristic modeling methods in clustering performance.

In this paper, we shall be concerned with a relation between TQFTs and cut and paste invariants introduced by Karras, Kreck, Neumann and Ossa. Cut and paste invariants, or SK invariants, are functions on the set of smooth manifolds that are invariant under the cutting and pasting operation. Central to the work in this …

2018-03-08abs ↗pdf ↗

Algorithms based on spectral graph cut objectives such as normalized cuts, ratio cuts and ratio association have become popular in recent years because they are widely applicable and simple to implement via standard eigenvector computations. Despite strong performance for a number of clustering tasks, spectral graph cu…

2014-10-29abs ↗pdf ↗

Study on semiconcavity of solutions to gradient obstacle problems on compact manifolds.

problem Gradient obstacle problems on compact Riemannian manifolds.
method Uniform semiconcavity estimates and fine convergence results for solutions and free boundaries.
result The elastic and λλ-elastic sets of solutions converge to the cut locus and λλ-cut locus of the manifold.

New method calculates cut locus on Riemannian manifolds using optimal transport.

problem Computing the cut locus on compact Riemannian manifolds.
method Characterization via optimal transport density solution of Monge-Kantorovich equations, numerical approximation.
result Proposed novel framework for numerical approximation of cut locus.