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.
Study rates of convergence for approximate solutions to linear ill-posed problems in Hilbert scales.
In the present paper we consider application of overcomplete dictionaries to solution of general ill-posed linear inverse problems. In the context of regression problems, there has been enormous amount of effort to recover an unknown function using such dictionaries. One of the most popular methods, lasso and its versi…
Novel method uses Gaussian process to estimate particle sizes from scattering data.
Paper optimizes estimation of quadratic functionals in nonparametric IV models.
ReTaSA tackles continuous target shift in regression problems.
Proposes debiasing strategy for ill-posed regression problems.
Functional PLS improves prediction and inference for scalar responses from functional predictors.
New method calibrates LV surfaces for exotic derivatives with smoother, more stable Greeks.
New framework assesses regularization norms in ill-posed problems, revealing L2 instability and proposing adaptive fractional RKHS solutions.
MCGDiff uses SGM to guide SMC for solving ill-posed linear inverse problems.
The Fredholm integral equation of the first kind improves solutions for ill-posed supervised learning problems with limited data.
This paper presents a unified geometric framework for the statistical analysis of a general ill-posed linear inverse model which includes as special cases noisy compressed sensing, sign vector recovery, trace regression, orthogonal matrix estimation, and noisy matrix completion. We propose computationally feasible conv…
Maximum likelihood estimation fails to be well-posed in Gaussian process regression.
Ridge leverage scores provide a balance between low-rank approximation and regularization, and are ubiquitous in randomized linear algebra and machine learning. Deterministic algorithms are also of interest in the moderately big data regime, because deterministic algorithms provide interpretability to the practitioner …
Paper introduces a Gibbs sampler for Bayesian inversion of ill-posed problems.
Researchers develop methods to recover agent behavior from sparse data using Gaussian processes.
A VAE model predicts material properties and microstructures.
Score-based models improve diffuse optical tomography accuracy.
Magnetoencephalography and electroencephalography (M/EEG) are non-invasive modalities that measure the weak electromagnetic fields generated by neural activity. Estimating the location and magnitude of the current sources that generated these electromagnetic fields is a challenging ill-posed regression problem known as…
We study Tikhonov regularization for solving ill--posed operator equations where the solutions are functions defined on surfaces. One contribution of this paper is an error analysis of Tikhonov regularization which takes into account perturbations of the surfaces, in particular when the surfaces are approximated by spl…
New method for adaptive estimation and inference in econometric models without knowing smoothness.
We study the geodesic X-ray transform on compact Riemannian surfaces with conjugate points. Regardless of the type of the conjugate points, we show that we cannot recover the singularities and therefore, this transform is always unstable (ill-posed). We describe the microlocal kernel of and relate it to the con…
Medical image reconstruction is typically an ill-posed inverse problem. In order to address such ill-posed problems, the prior distribution of the sought after object property is usually incorporated by means of some sparsity-promoting regularization. Recently, prior distributions for images estimated using generative …
Study tackles inverse problems on low-dimensional manifolds, proving stability and proposing a reconstruction algorithm.
We propose a new learning-based approach to solve ill-posed inverse problems in imaging. We address the case where ground truth training samples are rare and the problem is severely ill-posed - both because of the underlying physics and because we can only get few measurements. This setting is common in geophysical ima…
The paper tackles inverse uncertainty quantification in neutron noise analysis.
This work tackles uncertainty quantification in tomography reconstruction.
A new mathematical model for the Black-Scholes equation is proposed to forecast option prices. This model includes new interval for the price of the underlying stock as well as new initial and boundary conditions. Conventional notions of maturity time and strike prices are not used. The Black-Scholes equation is solved…
The paper analyzes reg-SGD for convex problems, proving convergence and quantifying the rate of convergence.
We consider the problem of optimal portfolio selection under forward investment performance criteria in an incomplete market. The dynamics of the prices of the traded assets depend on a pair of stochastic factors, namely, a slow factor (e.g. a macroeconomic indicator) and a fast factor (e.g. stochastic volatility). We …
This paper formulates and studies a general continuous-time behavioral portfolio selection model under Kahneman and Tversky's (cumulative) prospect theory, featuring S-shaped utility (value) functions and probability distortions. Unlike the conventional expected utility maximization model, such a behavioral model could…
This paper explores deep learning for improving X-ray CT image reconstruction from undersampled data.
Transformer learns context and regularization for ICL in inverse problems.
The paper develops SGD for estimating operators from data.
New method forecasts stock option prices accurately.
MDNs offer a data-efficient alternative to diffusion and flow models for multimodal scientific learning.
New method learns kernels in nonlocal operators robustly.
Learning with noisy labels, which aims to reduce expensive labors on accurate annotations, has become imperative in the Big Data era. Previous noise transition based method has achieved promising results and presented a theoretical guarantee on performance in the case of class-conditional noise. However, this type of a…
New method identifies causal relationships without strong assumptions.
Paper examines convergence rate of PGD for BP objective in inverse problems.
Signal retrieval from a series of indirect measurements is a common task in many imaging, metrology and characterization platforms in science and engineering. Because most of the indirect measurement processes are well-described by physical models, signal retrieval can be solved with an iterative optimization that enfo…
Learning non-linear systems from noisy, limited, and/or dependent data is an important task across various scientific fields including statistics, engineering, computer science, mathematics, and many more. In general, this learning task is ill-posed; however, additional information about the data's structure or on the …
This paper proposes a novel framework to regularize the highly ill-posed and non-linear Fourier ptychography problem using generative models. We demonstrate experimentally that our proposed algorithm, Deep Ptych, outperforms the existing Fourier ptychography techniques, in terms of quality of reconstruction and robustn…
Method determines credit transition matrix from cumulative default probabilities.
New method for inference on strongly identified functionals even when nuisance functions are weakly identified.
Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution ena…
Variational Gaussian Processes solve linear inverse problems efficiently.
Iterative shrinkage/thresholding algorithm (ISTA) is a well-studied method for finding sparse solutions to ill-posed inverse problems. In this letter, we present a data-driven scheme for learning optimal thresholding functions for ISTA. The proposed scheme is obtained by relating iterations of ISTA to layers of a simpl…