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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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238475713950 · Jun 202019922001200920172026
48 results for Unbounded State Space

New approach to concentration inequalities for unbounded state space dynamical systems.

problem Concentration inequalities for unbounded state space dynamical systems.
method Functional analytic framework, transport-entropy inequality.
result Exponential concentration inequalities for sampling from stationary distribution.

Algorithm learns diffusion processes with high-dimensional state spaces.

problem Stochastic control of unbounded diffusion processes with high-dimensional state spaces.
method Adaptive partitioning and learning algorithm that refines discretization based on estimation bias and statistical confidence.
result Established regret bounds that depend on problem parameters, extending to unbounded diffusion processes.

GP-PSRL achieves sublinear regret for continuous control with unbounded state space.

problem Analyzing regret bounds for GP-PSRL in continuous control with unbounded state space.
method Recursive application of Borell-Tsirelson-Ibragimov-Sudakov inequality and chaining method.
result Sublinear regret bound of O~(HγTT)\widetilde{\mathcal{O}}(H\sqrt{γ_TT}) for GP-PSRL.

This paper deals with discrete-time Markov control processes on a general state space. A long-run risk-sensitive average cost criterion is used as a performance measure. The one-step cost function is nonnegative and possibly unbounded. Using the vanishing discount factor approach, the optimality inequality and an optim…

2007-04-03abs ↗pdf ↗

Develops thermodynamic formalism for quasimorphisms on negatively curved spaces.

problem Analyzing quasimorphisms on negatively curved spaces.
method Thermodynamic formalism framework, Banach isomorphism, weak Livšic cohomology.
result Establishes Central Limit Theorem and invariance principle for unbounded quasimorphisms.

Symplectic method solves infinite-dimensional Schrödinger equations.

problem Solving Schrödinger equations on infinite-dimensional Hilbert spaces with unbounded Hamiltonians.
method Analytic vectors, manifolds modelled on normed spaces, symplectic differential geometry, Marsden--Weinstein reduction.
result Mapped tt-dependent Schrödinger equations onto projective spaces.

New algorithm tackles multiclass transductive online learning with unbounded labels.

problem Characterizing optimal mistake bound for unbounded label spaces.
method Introducing new combinatorial dimensions (Level-constrained Littlestone and Branching dimensions) to characterize online learnability.
result Established trichotomy of possible minimax rates for unbounded label spaces: Θ(T)Θ(T), Θ(logT)Θ(\log T), or Θ(1)Θ(1).

New neural network rates for unbounded domains with weighted Sobolev spaces.

problem Improving neural network approximation rates for unbounded domains.
method Embedding results for weighted Fourier-Lebesgue spaces in weighted Sobolev spaces, followed by asymptotic approximation rates.
result Asymptotic approximation rates for shallow neural networks without curse of dimensionality for unbounded domains and Muckenhoupt weights.

New approach finds solutions to games with unbounded controls.

problem Existence of equilibrium in mean-field games with unbounded controls.
method Weak formulation and new existence/stability results for quadratic-growth generalized McKean-Vlasov BSDEs.
result Existence of equilibrium result for non-Markovian mean-field games with unbounded control space.

This paper solves the normalizability crisis in sequential inference by introducing bounded information geometry.

problem Structural failure in standard sequential inference architectures when dealing with extreme outliers.
method Non-parametric field actions and bounded information geometry to truncate infinite tails of spatial distributions.
result Empirical benchmarks across three domains show robust estimation without infinite-tailed distributional assumptions.

We study ancient solutions of polynomial growth to both continuous-time and discrete-time heat equations on graphs with unbounded Laplacians. We generalize Colding and Minicozzi's theorem [CM19] on manifolds, and the result [Hua19] on graphs with normalized Laplacians to the setting of graphs with unbounded Laplacians:…

2019-10-07abs ↗pdf ↗

Efficiently estimates quantiles and maximum in unbounded datasets with differential privacy.

problem Efficiently estimating quantiles and maximum in unbounded datasets with differential privacy.
method Simple invocation of a subroutine called AboveThreshold, iteratively called in Sparse Vector Technique.
result Improved estimates on highest quantiles with robustness and accuracy.

New analysis shows a gap between Gaussian RKHS and neural networks on unbounded domains.

problem Understanding the function space bias of neural networks compared to Gaussian RKHS.
method Infinite-center asymptotic analysis of neural network Banach space and Gaussian RKHS on unbounded domains.
result Certain functions in Gaussian RKHS have infinite norm in neural network Banach space on unbounded domains.

Solves open problem on universally consistent online learning with unbounded losses.

problem Open problem on universally consistent online learning with unbounded losses.
method Constructs random measurable partitions of the instance space.
result Simple memorization rule is optimistically universal for any unbounded loss.

Using results from our companion article [arXiv:1112.4824v2] on a Schauder approach to existence of solutions to a degenerate-parabolic partial differential equation, we solve three intertwined problems, motivated by probability theory and mathematical finance, concerning degenerate diffusion processes. We show that th…

2012-11-20abs ↗pdf ↗

Two new algorithms improve performance in adversarial bandits with unbounded losses.

problem Adversarial Multi-Armed Bandits with unbounded losses.
method Developed UMAB-NN and UMAB-G for non-negative and general unbounded losses respectively.
result UMAB-NN achieves the first adaptive and scale-free regret bound for non-negative unbounded losses.

We factorize the Dirac operator on the Connes-Landi 4-sphere in unbounded KK-theory. We show that a family of Dirac operators along the orbits of the torus action defines an unbounded Kasparov module, while the Dirac operator on the principal orbit space -an open quadrant in the 2-sphere- defines a half-closed chain. W…

2018-03-23abs ↗pdf ↗

A new active learning method for Gaussian process models.

problem Efficiently exploring unbounded state spaces for accurate models.
method Maximizes mutual information with respect to a bounded region using model predictive control.
result Our method yields a better model within the region of interest than entropy-based methods.

We produce solutions to the Kähler-Ricci flow emerging from complete initial metrics g0g_0 which are C0C^0 Hermitian limits of Kähler metrics. Of particular interest is when g0g_0 is Kähler with unbounded curvature. We provide such solutions for a wide class of U(n)U(n)-invariant Kähler metrics g0g_0 on nn dimensional c…

2014-02-26abs ↗pdf ↗

LLA shows strong performance in Bayesian optimization but has unbounded search space issues.

problem Applying LLA in unbounded search spaces for Bayesian optimization.
method Linearized-Laplace approximation applied to Bayesian optimization problems.
result LLA demonstrates strong performance but also presents unbounded search space challenges.

A real valued function φ\varphi of one variable is called a metric transform if for every metric space (X,d)(X,d) the composition dφ=φdd_\varphi = \varphi\circ d is also a metric on XX. We give a complete characterization of the class of approximately nondecreasing, unbounded metric transforms φ\varphi such that the trans…

2017-10-13abs ↗pdf ↗

We consider the notion of dimension in four categories: the category of (unbounded) separable metric spaces and (metrically proper) Lipschitz maps, and the category of (unbounded) separable metric spaces and (metrically proper) uniform maps. A unified treatment is given to the large scale dimension and the small scale …

2006-07-10abs ↗pdf ↗

We prove a conjecture about approximating Gaussian Processes on one dimension.

problem Computational scaling issues with Gaussian Processes on one dimension.
method Developed a new family of state-space models (LEG) to approximate any stationary GP on one dimension.
result Proved that any stationary GP on one dimension can be approximated using the LEG family.

We establish the factorization of Dirac operators on Riemannian submersions of compact spinc^c manifolds in unbounded KK-theory. More precisely, we show that the Dirac operator on the total space of such a submersion is unitarily equivalent to the tensor sum of a family of Dirac operators with the Dirac operator on th…

2016-10-10abs ↗pdf ↗

We introduce the quasi-hyperbolicity constant of a metric space, a rough isometry invariant that measures how a metric space deviates from being Gromov hyperbolic. This number, for unbounded spaces, lies in the closed interval [1,2][1,2]. The quasi-hyperbolicity constant of an unbounded Gromov hyperbolic space is equal to…

2019-08-12abs ↗pdf ↗

The Einstein/Abelian-Yang-Mills Equations reduce in the stationary and axially symmetric case to a harmonic map with prescribed singularities $\p\colon\R^3\smΣ\to\H^{k+1}_\C$ into the (k+1)(k+1)-dimensional complex hyperbolic space. In this paper, we prove the existence and uniqueness of harmonic maps with prescribed sing…

1995-09-19abs ↗pdf ↗

This memoir presents a systematic study of the utility maximization problem of an investor in a constrained and unbounded financial market. Building upon the work of Hu et al. (2005) [Ann. Appl. Probab., 15, 1691--1712] in a bounded framework, we extend our analysis to the more challenging unbounded case. Our methodolo…

2017-07-01abs ↗pdf ↗

Study of LQ MFGs in infinite-dimensional Hilbert spaces.

problem Mean field games in infinite-dimensional settings with stochastic dynamics.
method Analysis of coupled semilinear infinite-dimensional stochastic evolution equations, development of Nash equilibrium.
result Characterization of unique Nash equilibrium in the limit of many agents.

It is shown that the compactly supported identity component of the diffeomorphism group of the 2-dimensional punctured torus Tp2\mathbb T^2_p is an unbounded group. It follows that the fragmentation norm of Tp2\mathbb T^2_p is unbounded.

2011-03-18abs ↗pdf ↗

New inequalities for unbounded functions improve denoising score matching.

problem Statistical error bounds for denoising score matching with unbounded objective functions.
method Derive new concentration inequalities using McDiarmid's inequality and Rademacher complexity bounds.
result Improved statistical error bounds for denoising score matching.

Study focal surfaces of wave fronts with unbounded curvatures.

problem Characterizing singularities of focal surfaces near non-degenerate singular points.
method Characterizations based on types of singularities and geometrical properties of initial fronts.
result Investigation of Gaussian curvature behavior of focal surfaces.

We introduce a general framework for measuring risk in the context of Markov control processes with risk maps on general Borel spaces that generalize known concepts of risk measures in mathematical finance, operations research and behavioral economics. Within the framework, applying weighted norm spaces to incorporate …

2011-10-28abs ↗pdf ↗