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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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76152228304 · Jun 202019922001200920172026
48 results for operating contexts

Continuum transformers learn operators in context via gradient descent.

problem Generalizing transformers to handle infinite-dimensional inputs for in-context learning.
method Gradient descent in an operator RKHS, leveraging generalized representer theorems and gradient flows.
result Operator learned in context is Bayes Optimal Predictor in infinite depth limit.

Study on neural scaling laws for solving linear systems in-context.

problem Theoretical guarantees for solving linear systems using a linear transformer architecture.
method Neural scaling laws and task diversity for in-domain and out-of-domain generalization.
result Novel notion of task diversity for necessary and sufficient condition of generalization under task shifts.

New operations defined on moduli spaces for bundles with orientations.

problem Pushforward operations for principal bundles with orientations.
method Developed a general theory of pushforward operations for principal GG-bundles, constructing specific operations for G=BU(1)G=BU(1).
result Classified all stable pushforward operations and showed they are generated by the projective Euler and rank operations.

ICON-OCnet solves optimal execution problems with neural networks and few examples.

problem Optimal order execution in markets with unknown price impact.
method Transformer-based neural network architecture (ICON-OCnet) that learns price impact from few examples and applies it to optimal execution strategies.
result ICON-OCnet accurately infers price impact models and retrieves optimal execution strategies for various propagator kernels.

Positive definite operator-valued kernels generalize the well-known notion of reproducing kernels, and are naturally adapted to multi-output learning situations. This paper addresses the problem of learning a finite linear combination of infinite-dimensional operator-valued kernels which are suitable for extending func…

2012-03-07abs ↗pdf ↗

We investigate index theory in the context of Dirac operators coupled to superconnections. In particular, we prove a local index theorem for such operators, and for families of such operators. We investigate eta-invariants and prove an APS-theorem, and construct a geometric determinant line bundle for families of such …

2008-10-05abs ↗pdf ↗

The time evolution operator KK is introduced in the graded context and its main properties are discussed. In particular, the operator KK is used to analize the projectability of constraint functions arising in the Lagrangian formalism for singular Lagrangians.

2001-12-13abs ↗pdf ↗

ICON learns differential equation operators from prompts, reducing retraining and improving few-shot learning.

problem Training neural networks to solve differential equations without retraining for new problems.
method In-Context Operator Networks (ICON) that learns operators from prompted data and applies them to new problems.
result ICON can generalize to new operators beyond the training distribution and requires only a few demos.

ICON learns differential equation operators from examples, revealing probabilistic inference.

problem Learning operators for differential equations from limited examples.
method Probabilistic operator learning using ICON architectures trained on diverse datasets.
result ICON implicitly performs Bayesian inference on solution operators.

We show how the machine of PROP profiles invented by S. Merkulov can be used to study and classify natural operators in differential geometry. We also give an interpretation of graph complexes arising in this context in terms of representation theory. As application, we prove several results on classification of natura…

2006-12-07abs ↗pdf ↗

In the context of synthetic differential geometry, we study the Laplace operator an a Riemannian manifold. The main new aspect is a neighbourhood of the diagonal, smaller than the second neighbourhood usually required as support for second order differential operators. The new neighbourhood has the property that a func…

2000-06-23abs ↗pdf ↗

We derive an inequality that relates nodal set and eigenvalues of a class of twisted Dirac operators on closed surfaces and point out how this inequality naturally arises as an eigenvalue estimate for the Spinc\rm Spin^c Dirac operator. This allows us to obtain eigenvalue estimates for the twisted Dirac operator appearing…

2016-01-28abs ↗pdf ↗

New method solves high-dimensional Bayesian inverse problems efficiently.

problem Efficiently solving high-dimensional Bayesian inverse problems with limited data.
method Physics-informed Neural Operators with RealNVP architecture for invertibility and differentiability.
result Accurate approximations of the full posterior without additional forward solves or sampling.

Transformers can predict new tokens based on any number of context tokens, approximating continuous mappings with fixed resources.

problem Handling an arbitrarily large number of context tokens in transformers.
method Mathematical analysis of transformer's expressivity using Wasserstein distance and continuous mappings.
result Deep transformers are universal and can approximate continuous in-context mappings to arbitrary precision, uniformly over compact token domains.

New eigenvalue estimate for CR manifolds' Kohn-Dirac operator.

problem Estimating eigenvalues of the Kohn-Dirac operator on CR manifolds.
method Characterizing equality case by CR twistor spinor existence; classifying manifolds with specific Ricci tensor properties.
result Classifying CR manifolds with at most two Webster Ricci tensor eigenvalues.

In this article, we introduce the notion of cycling operations of arbitrary order in Garside groups, which is a full generalization of the cycling and decycling operations. Theoretically, this notion together with other related concepts provides a context in which various definitions and arguments concerning Garside gr…

2006-05-30abs ↗pdf ↗

Motivated by the study of Hörmander's sums-of-squares operators and their generalizations, we define the convolution algebra of transverse distributions associated to a singular foliation. We prove that this algebra is represented as continuous linear operators on the spaces of smooth functions and generalized function…

2019-10-07abs ↗pdf ↗

The aim of this paper is to introduce new forms of the weak and Omori-Yau maximum principles for linear operators, notably for trace type operators, and show their usefulness, for instance, in the context of PDE's and in the theory of hypersurfaces. In the final part of the paper we consider a large class of non-linear…

2013-03-20abs ↗pdf ↗

Defines Wodzicki residue using groupoids and fibered distributions.

problem Defining and understanding the Wodzicki residue in noncommutative geometry.
method Using groupoid language and filtered manifolds, defining the residue and showing its properties.
result The groupoidal residue is a trace on pseudodifferential operators and matches the usual residue in certain cases.

We prove existence, uniqueness, and regularity of viscosity solutions to the stationary and evolution obstacle problems defined by a class of nonlocal operators that are not stable-like and may have supercritical drift. We give sufficient conditions on the coefficients of the operator to obtain Hölder and Lipschitz con…

2017-09-29abs ↗pdf ↗

We propose a generalization of the classical notions of plumbing and Murasugi summing operations to smooth manifolds of arbitrary dimensions, so that in this general context Gabai's credo "the Murasugi sum is a natural geometric operation" holds. In particular, we prove that the sum of the pages of two open books is ag…

2014-12-06abs ↗pdf ↗

Devoted to multi-task learning and structured output learning, operator-valued kernels provide a flexible tool to build vector-valued functions in the context of Reproducing Kernel Hilbert Spaces. To scale up these methods, we extend the celebrated Random Fourier Feature methodology to get an approximation of operator-…

2016-05-09abs ↗pdf ↗

We extend the Atiyah, Patodi, and Singer index theorem for first order differential operators from the context of manifolds with cylindrical ends to manifolds with periodic ends. This theorem provides a natural complement to Taubes' Fredholm theory for general end-periodic operators. Our index theorem is expressed in t…

2011-05-02abs ↗pdf ↗

This study explores the index theory of Heisenberg elliptic and transversally Heisenberg elliptic operators using KKKK-theory.

problem Analyzing the index theory of Heisenberg elliptic and transversally Heisenberg elliptic operators.
method Applying Kasparov's methodology and examining specific conditions using Fourier transform of the nilpotent group CC^*-algebra.
result Demonstrated enhanced methods for analyzing hypoellipticity and defined transversal Heisenberg ellipticity in a KKKK-theoretic context.

We study a Dirichlet-to-Neumann eigenvalue problem for differential forms on a compact Riemannian manifold with smooth boundary. This problem is a natural generalization of the classical Steklov problem on functions. We derive a number of upper and lower bounds for the first eigenvalue in several contexts: many of thes…

2011-05-13abs ↗pdf ↗

Understanding how funding and 4H context regulate crypto markets.

problem Analyzing the chaotic appearance of financial markets.
method Observing interactions between market context and capital conditions in the 4H timeframe.
result Ranges in crypto markets are strategic positioning by informed participants, not indecision.

New analysis proves sketching operators' RIP guarantees for mixture models without importance sampling.

problem Proving sketching operators' Restricted Isometry Property (RIP) for mixture models without assuming importance sampling.
method Proposed alternative analysis based on new deterministic bounds and concentration inequalities.
result Theoretical guarantees for sketching operators without importance sampling.

The Serre-Swan theorem in differential geometry establishes an equivalence between the category of smooth vector bundles over a smooth compact manifold and the category of finitely generated projective modules over the unital ring of smooth functions. This theorem is here generalized to manifolds of bounded geometry. I…

2013-02-14abs ↗pdf ↗

VectorNet predicts car behavior using vectorized HD maps and agent dynamics.

problem Predicting behavior in multi-agent systems with self-driving cars.
method VectorNet uses hierarchical graph neural networks on vectorized representations of HD maps and agent trajectories.
result VectorNet achieves comparable or better performance than state-of-the-art methods while using fewer parameters and less computational power.

Study on estimating distances between covariance operators and Gaussian processes.

problem Estimating distances between covariance operators and Gaussian processes.
method Riemannian distances, concentration results for Hilbert space-valued random variables, RKHS covariance and cross-covariance operators.
result Both distances converge in the Hilbert-Schmidt norm and can be consistently and efficiently estimated.

We introduce the notion of Ricci-corrected differentiation in parabolic geometry, which is a modification of covariant differentiation with better transformation properties. This enables us to simplify the explicit formulae for standard invariant operators given in work of Cap, Slovak and Soucek, and at the same time e…

2003-10-20abs ↗pdf ↗

Develops trace class operators and inverse Laplacian theory for infinite dimensions.

problem Understanding trace class operators and inverse Laplacian on infinite dimensional spaces.
method Presentation of trace class operators and construction of inverse Laplacian on closed manifolds.
result Original trace computations involving the inverse Laplacian on the torus.

We show that the (graded) spectral flow of a family of Toeplitz operators on a complete Riemannian manifold is equal to the index of a certain Callias-type operator. When the dimension of the manifold is even this leads to a cohomological formula for the spectral flow. As an application, we compute the spectral flow of…

2018-03-29abs ↗pdf ↗

We consider generalized gradients in the general context of GG-structures. They are natural first order differential operators acting on sections of vector bundles associated to irreducible GG-representations. We study their geometric properties and show in particular their conformal invariance.

2009-08-17abs ↗pdf ↗

Derives an index formula for families of end-periodic Dirac operators.

problem Calculating the index of families of end-periodic Dirac operators.
method Using the renormalized Chern character and Fourier-Laplace transform of the Bismut superconnection.
result Establishes an index formula involving a new end-periodic eta form.