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.
SINGD improves KFAC for memory-efficiency and stability in low-precision training.
The authors aim to develop numerical schemes of the two representative quadratic hedging strategies: locally risk minimizing and mean-variance hedging strategies, for models whose asset price process is given by the exponential of a normal inverse Gaussian process, using the results of Arai et al. \cite{AIS}, and Arai …
We present a numerical implementation of the geodesic ray transform and its inversion over functions and solenoidal vector fields on two-dimensional Riemannian manifolds. For each problem, inversion formulas previously derived in \cite{Pestov2004,Krishnan2010} are implemented in the case of simple and some non-simple m…
New findings on mesh group-planes validate Signature-inverse Theorem under specific conditions.
The paper derives formulas for option pricing and random walk expectations.
This paper tackles regularization parameter learning in inverse problems using data-driven bilevel optimization.
The original Broad Learning System (BLS) on new added nodes and its existing efficient implementation both assume the ridge parameter lambda -> 0 in the ridge inverse to approximate the generalized inverse, and compute the generalized inverse solution for the output weights. In this paper, we propose two ridge solution…
Machine learning models solve inverse eigenvalue problems for symmetric potentials and refractive indices.
The inverse-free extreme learning machine (ELM) algorithm proposed in [4] was based on an inverse-free algorithm to compute the regularized pseudo-inverse, which was deduced from an inverse-free recursive algorithm to update the inverse of a Hermitian matrix. Before that recursive algorithm was applied in [4], its impr…
New filters improve radar target inference in complex scenarios.
This work tackles exploding inverses in INNs, revealing and mitigating their numerical non-invertibility.
Regularizes 3D inverse scattering with tangent-point energy for better solutions.
Develops a new method to compute risk-sharing allocations using Laplace transforms.
Likelihood-free (a.k.a. simulation-based) inference problems are inverse problems with expensive, or intractable, forward models. ODE inverse problems are commonly treated as likelihood-free, as their forward map has to be numerically approximated by an ODE solver. This, however, is not a fundamental constraint but jus…
Proves convexity of level sets of general inverse σ_k equations.
Study uses machine learning to solve photoacoustic tomography's inverse problem.
Method estimates sparse inverse covariance and partial correlation matrices efficiently.
Enhances OTA FL algorithms by defining inverse feasibility for linear models.
New efficient method for inverse Z-transform reduces complexity significantly.
Inverse optimal transport (OT) refers to the problem of learning the cost function for OT from observed transport plan or its samples. In this paper, we derive an unconstrained convex optimization formulation of the inverse OT problem, which can be further augmented by any customizable regularization. We provide a comp…
We consider the problem of joint estimation of structured inverse covariance matrices. We perform the estimation using groups of measurements with different covariances of the same unknown structure. Assuming the inverse covariances to span a low dimensional linear subspace in the space of symmetric matrices, our aim i…
Paired autoencoders solve inverse problems using latent space projections.
We show, analytically and numerically, that wealth distribution in the Bouchaud-Mézard network model of the economy is described by a three-parameter generalized inverse gamma distribution. In the mean-field limit of a network with any two agents linked, it reduces to the inverse gamma distribution.
The wavelet Maximum Entropy on the Mean (wMEM) approach to the MEG inverse problem is revisited and extended to infer brain activity from full space-time data. The resulting dimensionality increase is tackled using a collection of techniques , that includes time and space dimension reduction (using respectively wavelet…
A new method for efficiently computing derivatives of skew-symmetric matrix exponentials.
MCGDiff uses SGM to guide SMC for solving ill-posed linear inverse problems.
This study proposes an efficient surrogate for Darcy flow inverse problems.
Deep neural networks solve noisy, complex problems accurately.
New method uses deep learning to solve inverse problems with provable guarantees.
Bayesian inverse problems solved with Gaussian models for PDEs.
This paper revisits the Bayesian CMA-ES and provides updates for normal Wishart. It emphasizes the difference between a normal and normal inverse Wishart prior. After some computation, we prove that the only difference relies surprisingly in the expected covariance. We prove that the expected covariance should be lower…
Bayesian ANN method predicts chaotic systems with uncertainty.
ML surrogates speed up Bayesian inverse problem solving.
This work characterizes, analytically and numerically, two major effects of the quadratic Wasserstein () distance as the measure of data discrepancy in computational solutions of inverse problems. First, we show, in the infinite-dimensional setup, that the distance has a smoothing effect on the inversion pro…
We study solutions to the inverse mean curvature flow which evolve by homotheties of a given submanifold with arbitrary dimension and codimension. We first show that the closed ones are necessarily spherical minimal immersions and so we reveal the strong rigidity of the Clifford torus in this setting. Mainly we focus o…
A framework uses variational Bayes for solving inverse problems efficiently.
The present paper studies so-called deep image prior (DIP) techniques in the context of ill-posed inverse problems. DIP networks have been recently introduced for applications in image processing; also first experimental results for applying DIP to inverse problems have been reported. This paper aims at discussing diff…
A new method simulates square-root processes efficiently.
New criterion for solving inverse Hessian equations, including J-equation.
A non-parametric method for evaluation of the aggregate loss distribution (ALD) by combining and numerically inverting the empirical characteristic functions (CFs) is presented and illustrated. This approach to evaluate ALD is based on purely non-parametric considerations, i.e., based on the empirical CFs of frequency …
We develop a geometric version of the inverse problem of the calculus of variations for discrete mechanics and constrained discrete mechanics. The geometric approach consists of using suitable Lagrangian and isotropic submanifolds. We also provide a transition between the discrete and the continuous problems and propos…
Develops an inverse particle filter for cognitive systems.
In this study, a numerical quadrature for the generalized inverse Gaussian distribution is derived from the Gauss-Hermite quadrature by exploiting its relationship with the normal distribution. The proposed quadrature is not Gaussian, but it exactly integrates the polynomials of both positive and negative orders. Using…
The paper stabilizes invertible neural networks by using Gaussian mixture models.
By introducing a shape manifold as a solution set to solve inverse obstacle scattering problems we allow the reconstruction of general, not necessarily star-shaped curves. The bending energy is used as a stabilizing term in Tikhonov regularization to gain independence of the parametrization. Moreover, we discuss how se…
Inverse problems arise in a number of domains such as medical imaging, remote sensing, and many more, relying on the use of advanced signal and image processing approaches -- such as sparsity-driven techniques -- to determine their solution. This paper instead studies the use of deep learning approaches to approximate …
A deep learning approach solves probabilistic inverse problems with physical constraints.
New methods for -transform inversion and Wiener-Hopf factorization.