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arXiv research

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.

168,695 papers · 148 categories

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107214321428 · Jun 202019922001200920172026
48 results for higher order

A key feature of inductive logic programming (ILP) is its ability to learn first-order programs, which are intrinsically more expressive than propositional programs. In this paper, we introduce techniques to learn higher-order programs. Specifically, we extend meta-interpretive learning (MIL) to support learning higher…

2019-07-25abs ↗pdf ↗

For certain classes of knots we define geometric invariants called higher-order genera. Each of these invariants is a refinement of the slice genus of a knot. We find lower bounds for the higher-order genera in terms of certain von Neumann ρρ-invariants, which we call higher-order signatures. The higher-order genera o…

2008-07-02abs ↗pdf ↗

A fundamental property of complex networks is the tendency for edges to cluster. The extent of the clustering is typically quantified by the clustering coefficient, which is the probability that a length-2 path is closed, i.e., induces a triangle in the network. However, higher-order cliques beyond triangles are crucia…

2017-04-12abs ↗pdf ↗

Higher-order tangent bundles have geometric structures compatible with their iterated bundle structure.

problem Connection towers and Sasaki metrics on higher-order tangent bundles
method Introduce the notion of a connection tower and study the geometric structures induced by such towers.
result Connection towers determine multiconnections, adapted splittings, and canonical vector bundle structures.

In this paper we develop a geometric approach to higher order mechanics on graded bundles in both, the Lagrangian and Hamiltonian formalism, via the recently discovered weighted algebroids. We present the corresponding Tulczyjew triple for this higher order situation and derive in this framework the phase equations fro…

2014-12-08abs ↗pdf ↗

This paper presents the first use of graph neural networks (GNNs) for higher-order proof search and demonstrates that GNNs can improve upon state-of-the-art results in this domain. Interactive, higher-order theorem provers allow for the formalization of most mathematical theories and have been shown to pose a significa…

2019-05-24abs ↗pdf ↗

The paper glosses different forms of an introducing of higher order tangent-like functors, especially functors derived from higher order nonholonomic tangent functors. A special attention is devoted to higher order osculating bundles: their identification with higher order tangent bundles is demonstrated as the main re…

2012-02-13abs ↗pdf ↗

A new method predicts higher-order interactions in evolving graphs using simplicial complexes.

problem Predicting higher-order interactions in dynamic graphs with theoretical guarantees.
method Capturing higher-order interactions as simplices, modeling neighborhoods with face-vectors, and developing a nonparametric kernel estimator.
result Our method outperforms existing higher-order prediction methods and is theoretically consistent.

Novel higher-order group synchronization for noisy local measurements on hypergraphs.

problem Synchronizing higher-order local measurements on hyperedges to global estimates on nodes.
method Message passing algorithm for global synchronization of higher-order measurements.
result Higher-order method outperforms standard pairwise synchronization methods in certain applications.

The paper connects higher order risk measures and stochastic dominance, showing their equivalence and integrating them with optimization.

problem Comparing and characterizing random outcomes in risk assessment.
method Exploring the equivalence between higher order risk measures and stochastic dominance, using stochastic optimization and expectiles as examples.
result Higher order risk measures and stochastic dominance are equivalent and can be used to characterize random outcomes.

Graph Convolution Network (GCN) has been recognized as one of the most effective graph models for semi-supervised learning, but it extracts merely the first-order or few-order neighborhood information through information propagation, which suffers performance drop-off for deeper structure. Existing approaches that deal…

2019-11-11abs ↗pdf ↗

We construct new examples of algebraic curvature tensors so that the Jordan normal form of the higher order Jacobi operator is constant on the Grassmannian of subspaces of type (r,s)(r,s) in a vector space of signature (p,q)(p,q). We then use these examples to establish some results concerning higher order Osserman and highe…

2002-05-07abs ↗pdf ↗

State-of-the-art methods in convex and non-convex optimization employ higher-order derivative information, either implicitly or explicitly. We explore the limitations of higher-order optimization and prove that even for convex optimization, a polynomial dependence on the approximation guarantee and higher-order smoothn…

2017-10-27abs ↗pdf ↗

This work explores higher-order algebroids via vector bundle comorphisms.

problem Generalizing concepts of higher-order tangent bundles and Lie algebroids.
method Introduces a vector bundle comorphism approach to describe higher-order algebroids.
result Establishes a one-to-one correspondence between higher-order Lie algebroids and specific algebraic structures.

Introduce Collapsed Effective Operators for higher-order structures.

problem Existing spectral operators decompose topology into separate ranks, leaving practitioners to fuse information back to vertices.
method Introduce Collapsed Effective Operators via Schur complementation of a graded Laplacian.
result Preserves positive semi-definiteness, lowers system energy under higher-order connectivity.

New estimator stabilizes higher-order influence functions for stable statistical inference.

problem Numerical instability in estimating inverse population Gram matrix.
method Proposes a new stabilized higher-order estimator without sample splitting.
result Stabilized estimator exhibits more stable performance and similar statistical guarantees.

Given a closed, oriented, connected 3-manifold, M, we define higher-order linking forms on the higher-order Alexander modules of M. These higher-order linking forms generalize similar linking forms for knots previously studied by the author, which were themselves generalizations of the classical Blanchfield linking for…

2012-04-23abs ↗pdf ↗

We present a geometric approach to the field theory with higher order anisotropic interactions. The concepts of higher order space, or locally anisotropic, space (in brief, h-space, or la-space) are introduced as general ones for various types of higher order extensions of Lagrange and Finsler geometry and higher dimen…

1996-11-09abs ↗pdf ↗

Combines neural networks and probabilistic graphical models for efficient higher-order inference.

problem Lack of efficient higher-order relational information in graph neural networks and probabilistic graphical models.
method Derives efficient approximate sum-product loopy belief propagation for higher-order PGMs, embeds into neural network, proposes methods for constructing higher-order factors.
result Substantially outperforms state-of-the-art k-order graph neural networks in molecular datasets.

Unified approach to discrete and smooth isoperimetric inequalities of arbitrary order.

problem Finding higher order isoperimetric inequalities for both discrete and smooth curves.
method Unified approach via Fourier analysis of linear operators.
result Unified upper and lower bounds for isoperimetric deficit in smooth curves.

Improved GAN performance using higher-order Wasserstein moments.

problem Stabilizing and enhancing GANs for better mode coverage and stability.
method Deriving and training a GAN with a modified Wasserstein distance using higher-order moments.
result Training a GAN with higher-order Wasserstein moments improves performance, even with increased computational cost.

New estimator stabilizes higher-order influence functions for bilinear forms.

problem Stability issues in estimating bilinear forms using higher-order influence functions.
method Proposes a new stabilized higher-order estimator for a class of bilinear forms without sample splitting.
result New estimator exhibits more stable finite-sample performance compared to the empirical higher-order estimator.

Develops higher-order Euler-Poincaré field equations for principal G-bundles.

problem Formulating field equations for higher-order jet bundles of principal G-bundles.
method Reduction theory applied to GG-invariant Lagrangian field theories on jet bundles, transferring Hamilton's principle to reduced configuration bundles.
result Higher-order Euler-Poincaré field equations are equivalent to conservation of Noether current.

The paper introduces boundary operators for Poincaré-Einstein manifolds and proves higher order trace inequalities.

problem Proving higher order trace inequalities on Poincaré-Einstein manifolds.
method Introducing conformally covariant boundary operators and using them to set up higher order Dirichlet problems.
result Sharp higher order Sobolev trace inequalities on the ball are obtained.

We define the higher-order Alexander modules An,i(U)A_{n,i}(\mathcal{U}) and higher-order degrees δn,i(U)δ_{n,i}(\mathcal{U}) which are invariants of a complex hypersurface complement U\mathcal{U}. These invariants come from the module structure of the homology of certain solvable covers of the hypersurface complement. Such inv…

2015-10-12abs ↗pdf ↗

GUIDE detects anomalies in attributed networks by reconstructing node attributes and higher-order structures.

problem Lack of effective mechanisms for detecting anomalies in complex network interactions.
method GUIDE uses attribute and structure autoencoders, graph attention, and reconstruction errors to identify anomalies.
result GUIDE significantly outperforms state-of-the-art methods on multiple real-world datasets.

In this paper we derive the symplectic framework for field theories defined by higher-order Lagrangians. The construction is based on the symplectic reduction of suitable spaces of iterated jets. The possibility of reducing a higher-order system of PDEs to a constrained first-order one, the symplectic structures natura…

2014-08-09abs ↗pdf ↗

Representation learning on networks offers a powerful alternative to the oft painstaking process of manual feature engineering, and as a result, has enjoyed considerable success in recent years. However, all the existing representation learning methods are based on the first-order network (FON), that is, the network th…

2019-08-15abs ↗pdf ↗

This paper describes a general framework for learning Higher-Order Network Embeddings (HONE) from graph data based on network motifs. The HONE framework is highly expressive and flexible with many interchangeable components. The experimental results demonstrate the effectiveness of learning higher-order network represe…

2018-01-28abs ↗pdf ↗

We compare two ways of interpreting higher order connections. The geometric approach lies in the decomposition of higher order tangent space into the horizontal and vertical structures while the jet--like approach considers a higher order connection as the section of a jet prolongation of a fibered manifold. Particular…

2012-06-25abs ↗pdf ↗

Networks provide a powerful formalism for modeling complex systems by using a model of pairwise interactions. But much of the structure within these systems involves interactions that take place among more than two nodes at once; for example, communication within a group rather than person-to person, collaboration amon…

2018-02-20abs ↗pdf ↗

We provide improved convergence rates for various \emph{non-smooth} optimization problems via higher-order accelerated methods. In the case of \ell_\infty regression, we achieves an O(ε4/5)O(ε^{-4/5}) iteration complexity, breaking the O(ε1)O(ε^{-1}) barrier so far present for previous methods. We arrive at a similar rate fo…

2019-06-04abs ↗pdf ↗

New model estimates higher-order interactions in stochastic processes using lower-dimensional projections.

problem Estimating higher-order interaction effects in stochastic processes with limited data.
method Additive Poisson Process (APP) combines information geometry and generalized additive models to model intensity functions in lower dimensions.
result The model can estimate higher-order intensity functions with sparse data.

The paper provides estimates for higher-order Ricci curvature along Kähler-Ricci flows.

problem Estimating higher-order curvature along Kähler-Ricci flows on compact Kähler manifolds.
method Proving uniform bounds for Ricci curvature and scalar curvature in various orders and norms.
result A geometric obstruction causes a specific third-order derivative of Ricci curvature to blow up at rate et/2e^{t/2}.

Generalizes Carathéodory form for higher-order field theories.

problem Extending the Carathéodory form to second and higher-order Lagrangians.
method Geometric operations applied to the Poincaré--Cartan form and Lepage forms.
result Generalized Carathéodory form for second and higher-order Lagrangians.