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.
Paper proposes BCNMCC for system identification with noisy input.
New method improves regression models by optimizing correntropy with variable center.
A new kernel function centers at different points improves robust learning.
As a robust nonlinear similarity measure in kernel space, correntropy has received increasing attention in domains of machine learning and signal processing. In particular, the maximum correntropy criterion (MCC) has recently been successfully applied in robust regression and filtering. The default kernel function in c…
Enhances robustness of BLS using MCC criterion.
Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian assumption. However, when the signals are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises, the performance of KF will deteriorate serious…
Robust PCA reduces to power iterations for outlier-resilient feature extraction.
Constrained adaptive filtering algorithms inculding constrained least mean square (CLMS), constrained affine projection (CAP) and constrained recursive least squares (CRLS) have been extensively studied in many applications. Most existing constrained adaptive filtering algorithms are developed under mean square error (…
Robust diffusion adaptive estimation algorithms based on the maximum correntropy criterion (MCC), including adaptation to combination MCC and combination to adaptation MCC, are developed to deal with the distributed estimation over network in impulsive (long-tailed) noise environments. The cost functions used in distri…
Paper studies MCCR models with scale parameters tending to zero, revealing optimal learning rate and comparing robustness.
The unscented transformation (UT) is an efficient method to solve the state estimation problem for a non-linear dynamic system, utilizing a derivative-free higher-order approximation by approximating a Gaussian distribution rather than approximating a non-linear function. Applying the UT to a Kalman filter type estimat…
The maximum correntropy criterion (MCC) has recently been successfully applied in robust regression, classification and adaptive filtering, where the correntropy is maximized instead of minimizing the well-known mean square error (MSE) to improve the robustness with respect to outliers (or impulsive noises). Considerab…
New tensor completion method reduces impact of outliers.
Study improves PM concentration forecasting using MCCR loss.
Adaptive sparseness enhances robust regression using MCC and ARD.
New insights into correntropy-based regression reveal robustness and unified approaches.
In hyperspectral images, some spectral bands suffer from low signal-to-noise ratio due to noisy acquisition and atmospheric effects, thus requiring robust techniques for the unmixing problem. This paper presents a robust supervised spectral unmixing approach for hyperspectral images. The robustness is achieved by writi…
A new asymmetric correntropy method improves robust adaptive filtering for asymmetric error distributions.
Study improves regression models for non-Gaussian noise and outliers using correntropy.
In this letter, we propose a method for period estimation in light curves from periodic variable stars using correntropy. Light curves are astronomical time series of stellar brightness over time, and are characterized as being noisy and unevenly sampled. We propose to use slotted time lags in order to estimate corrent…
New maximum score estimators using ReLU functions and deep neural networks.
Proposes SNML for selecting word2vec Skip-gram dimensionality.
Optimal downsampling improves GLM performance in imbalanced classification.
Paper proves method for calculating NML code length works for continuous models.
New proof shows coupling-based flows converge linearly to diagonalize data covariance.
This paper studies the nonparametric modal regression problem systematically from a statistical learning view. Originally motivated by pursuing a theoretical understanding of the maximum correntropy criterion based regression (MCCR), our study reveals that MCCR with a tending-to-zero scale parameter is essentially moda…
A Bayesian factor graph reduced to normal form consists in the interconnection of diverter units (or equal constraint units) and Single-Input/Single-Output (SISO) blocks. In this framework localized adaptation rules are explicitly derived from a constrained maximum likelihood (ML) formulation and from a minimum KL-dive…
Optimizes recommendation models using skew normal distribution.
A new method improves maximum margin criterion for better pattern analysis.
Nonlinear similarity measures defined in kernel space, such as correntropy, can extract higher-order statistics of data and offer potentially significant performance improvement over their linear counterparts especially in non-Gaussian signal processing and machine learning. In this work, we propose a new similarity me…
We consider the problem of transforming samples from one continuous source distribution into samples from another target distribution. We demonstrate with optimal transport theory that when the source distribution can be easily sampled from and the target distribution is log-concave, this can be tractably solved with c…
The paper extends a link criterion for Lipschitz normal embeddings to definable sets in o-minimal structures.
Paper proposes robust tensor regression method for tensor data analysis.
A new test statistic measures discrepancy between conditional distributions.
The Mean-Variance Criterion is equivalent to Second-order Stochastic Dominance under symmetric Elliptical distributions.
New method accurately evaluates asset pricing under uncertainty and ambiguity.
Residual flows are shown to approximate MMD well.
We classify the normal CR structures on and their automorphism groups. Together with [3], this closes the classification of normal CR structures on contact 3-manifolds. We give a criterion to compare 2 normal CR structures, and we show that the underlying contact structure is, up to homotopy, unique.
This paper improves normalizing flows by combining MLE and sliced-Wasserstein distance for better data fidelity.
For massive data, the family of subsampling algorithms is popular to downsize the data volume and reduce computational burden. Existing studies focus on approximating the ordinary least squares estimate in linear regression, where statistical leverage scores are often used to define subsampling probabilities. In this p…
Roy's `Safety First' criterion for selecting one risky asset from many is adapted to the case of non-normal returns, via Cornish Fisher expansion. The resulting investment objective is consistent with first order stochastic dominance, and is equal to the Sharpe ratio for the case of normal returns. An investor selectin…
Operational risk models commonly employ maximum likelihood estimation (MLE) to fit loss data to heavy-tailed distributions. Yet several desirable properties of MLE (e.g. asymptotic normality) are generally valid only for large sample-sizes, a situation rarely encountered in operational risk. In this paper, we study how…
Paper proposes efficient training for normalizing flows in Boltzmann generators.
Classifies normal stable Horikawa surfaces with smoothable singularities.
The paper strengthens the classical result of MLE convergence to a Gaussian distribution.
Paper proposes an algorithm for robust estimation using Huber's criterion.
This paper introduces Kernel-based Information Criterion (KIC) for model selection in regression analysis. The novel kernel-based complexity measure in KIC efficiently computes the interdependency between parameters of the model using a variable-wise variance and yields selection of better, more robust regressors. Expe…
A new path gradient estimator speeds up normalizing flows without sacrificing accuracy.