Tag: parameter
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Beyond Cross-Validation: Adaptive Parameter Selection for Kernel-Based Gradient Descents
Beyond Cross-Validation: Adaptive Parameter Selection for Kernel-Based Gradient Descents arXiv:2603.03401v1 Announce Type: new Abstract: This paper proposes a novel parameter selection strategy for kernel-based gradient descent (KGD) algorithms, integrating bias-variance analysis with the splitting method. We introduce the concept of empirical effective dimension to quantify iteration increments in KGD, deriving an adaptive parameter selection strategy…
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Privacy utility trade offs for parameter estimation in degree heterogeneous higher order networks
Privacy utility trade offs for parameter estimation in degree heterogeneous higher order networks arXiv:2602.03948v1 Announce Type: new Abstract: In sensitive applications involving relational datasets, protecting information about individual links from adversarial queries is of paramount importance. In many such settings, the available data are summarized solely through the degrees of the nodes in the network.…
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TPV: Parameter Perturbations Through the Lens of Test Prediction Variance
TPV: Parameter Perturbations Through the Lens of Test Prediction Variance arXiv:2512.11089v1 Announce Type: new Abstract: We identify test prediction variance (TPV) — the first-order sensitivity of model outputs to parameter perturbations around a trained solution — as a unifying quantity that links several classical observations about generalization in deep networks. TPV is a fully label-free…
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Physics-Informed Regression: Parameter Estimation in Parameter-Linear Nonlinear Dynamic Models
Physics-Informed Regression: Parameter Estimation in Parameter-Linear Nonlinear Dynamic Models arXiv:2508.19249v1 Announce Type: cross Abstract: We present a new efficient hybrid parameter estimation method based on the idea, that if nonlinear dynamic models are stated in terms of a system of equations that is linear in terms of the parameters, then regularized ordinary least squares can…
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Stochastic Trace Optimization of Parameter Dependent Matrices Based on Statistical Learning Theory
Stochastic Trace Optimization of Parameter Dependent Matrices Based on Statistical Learning Theory arXiv:2508.05764v1 Announce Type: new Abstract: We consider matrices $boldsymbol{A}(boldsymboltheta)inmathbb{R}^{mtimes m}$ that depend, possibly nonlinearly, on a parameter $boldsymboltheta$ from a compact parameter space $Theta$. We present a Monte Carlo estimator for minimizing $text{trace}(boldsymbol{A}(boldsymboltheta))$ over all $boldsymbolthetainTheta$, and determine the sampling amount so that…
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Fast Likelihood-Free Parameter Estimation for L’evy Processes
Fast Likelihood-Free Parameter Estimation for L’evy Processes arXiv:2505.01639v1 Announce Type: new Abstract: L’evy processes are widely used in financial modeling due to their ability to capture discontinuities and heavy tails, which are common in high-frequency asset return data. However, parameter estimation remains a challenge when associated likelihoods are unavailable or costly to compute. We propose…
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Are You Sure Your Posterior Makes Sense?
Are You Sure Your Posterior Makes Sense? This article is co-authored by Felipe Bandeira, Giselle Fretta, Thu Than, and Elbion Redenica. We also thank Prof. Carl Scheffler for his support. Introduction Parameter estimation has been for decades one of the most important topics in statistics. While frequentist approaches, such as Maximum Likelihood Estimations, used to…
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Bayesian Model Parameter Learning in Linear Inverse Problems with Application in EEG Focal Source Imaging
Bayesian Model Parameter Learning in Linear Inverse Problems with Application in EEG Focal Source Imaging arXiv:2501.13109v1 Announce Type: cross Abstract: Inverse problems can be described as limited-data problems in which the signal of interest cannot be observed directly. A physics-based forward model that relates the signal with the observations is typically needed. Unfortunately, unknown model…