New method improves regression models by optimizing correntropy with variable center.
problem Improving regression models by optimizing correntropy with variable center.
method Proposed a new optimization criterion called Maximum Correntropy Criterion with Variable Center (MCC-VC) and an efficient approach to optimize kernel width and center location.
result Simulation results show desirable performance of the new method.
This work analyzes centered binary Restricted Boltzmann Machines (RBMs) and binary Deep Boltzmann Machines (DBMs), where centering is done by subtracting offset values from visible and hidden variables. We show analytically that (i) centering results in a different but equivalent parameterization for artificial neural …
Proposes a new ridge estimator for smooth covariates with adaptive centering.
problem Estimating coefficients and center function for smooth covariates in linear models.
method SACR framework with convex formulation, roughness penalty, and adaptive centering.
result Improves prediction and variable selection for smooth covariates.
Hierarchical Bayesian networks and neural networks with stochastic hidden units are commonly perceived as two separate types of models. We show that either of these types of models can often be transformed into an instance of the other, by switching between centered and differentiable non-centered parameterizations of …
Novel method uses image descriptors to harmonize MRI brain volumes across centers.
problem Inconsistencies in MRI brain volume measurements across different centers and scanners.
method Trained a Relevance Vector Machine (RVM) model using image descriptors to harmonize brain volumes.
result Decreases scanner and center variability while preserving measurements for longitudinal studies.
We propose a methodology to explore and measure the pairwise correlations that exist between variables in a dataset. The methodology leverages copulas for encoding dependence between two variables, state-of-the-art optimal transport for providing a relevant geometry to the copulas, and clustering for summarizing the ma…
The paper analyzes mean-field variational Bayes for complex models and proposes new uncertainty quantification methods.
problem Approximating posterior distributions in complex Bayesian models with latent variables.
method Non-asymptotic analysis on mean-field variational inference, showing that a normal distribution with the MLE center approximates the posterior well.
result The mean-field approximation matches the MLE up to higher-order terms and is essentially efficient for regular parametric models.
Covariance is shown as a commutator in random variable calculus.
problem Expressing covariance as a commutator of operators.
method Demonstrated through commutator identities involving expectations and products of functions.
result Revealed the underlying Lie algebraic structure in efficient influence curve calculus.
Dynamic probabilistic forecasts guide optimal decisions in uncertain processes.
problem Optimal decision making in processes influenced by uncertain random factors.
method Stochastic models for probabilistic forecast evolution, calibrated from ensemble forecasts.
result Optimal decision strategies determined using dynamic probabilistic forecasts.
Recently, a general method for analyzing the statistical accuracy of the EM algorithm has been developed and applied to some simple latent variable models [Balakrishnan et al. 2016]. In that method, the basin of attraction for valid initialization is required to be a ball around the truth. Using Stein's Lemma, we exten…
In this paper, we present a new R package COREclust dedicated to the detection of representative variables in high dimensional spaces with a potentially limited number of observations. Variable sets detection is based on an original graph clustering strategy denoted CORE-clustering algorithm that detects CORE-clusters,…
We study the problem of stochastic optimization for deep learning in the parallel computing environment under communication constraints. A new algorithm is proposed in this setting where the communication and coordination of work among concurrent processes (local workers), is based on an elastic force which links the p…
NKI integrates obfuscated datasets using nonlinear kernels for improved data collaboration.
problem Privacy-preserving data collaboration with reduced reconstruction risk.
method Formulates linear kernel integration, kernelizes it, and introduces graph regularization and centering constraints.
result NKI improves classification accuracy over existing linear integration methods under nonlinear dimensionality reduction.
Study tests how U.S. equity prices align with global asset frequencies using financial variables.
problem Testing whether U.S. equity prices align with global asset frequencies using financial variables.
method Examines SPX and RUT gaps, uses OIS-based funding, volatility, trading-friction, financial-condition variables, and residual information.
result Gains in fit survive broad-dollar neutralization, alternative blocks, PCA, residualization, and nested horizon selection, supporting reduced-form P-Q alignment.
We study Lagrangian submanifolds foliated by (n-1)-spheres in R^2n for n>2. We give a parametrization valid for such submanifolds, and refine that description when the submanifold is special Lagrangian, self-similar or Hamiltonian stationary. In all these cases, the submanifold is centered, i.e. invariant under the act…
A new kernel function centers at different points improves robust learning.
problem Robust learning in the presence of outliers.
method Introduces multi-kernel correntropy (MKC) with kernels centered at different points.
result Learning algorithms using MMKCC outperform those using MCC and MMCC.
The k-means++ algorithm is generalized by choosing the most distant point from the nearest center.
problem Improving the initialization of k-means clustering.
method Generalizing the center initialization process by selecting the most distant point from the nearest center.
result Choosing the most distant point from the nearest center achieves similar clustering quality to k-means++.
A new method centers outliers in robust PCA without manual intervention.
problem Outliers in robust PCA require manual centering, complicating the analysis.
method Introduces a 'bias trick' to automatically center non-outliers.
result First optimal RPCA algorithm with automatic centering.
Center identified in stated skein algebra for quantum traces.
problem Understanding the center of the stated skein algebra.
method Analyzing the algebra as a generalization of Kauffman bracket skein algebra, focusing on the case when the quantum parameter is a root of unity.
result Simple description and dimension calculation of the center over the center module.
Bayesian framework tackles measurement error in covariates.
problem Misleading inference due to corrupted covariates.
method Bayesian Nonparametric Learning framework robust to misspecification.
result General framework for Classical and Berkson error models.
The object of our investigation is a point that gives the maximum value of a potential with a strictly decreasing radially symmetric kernel. It defines a center of a body in Rm. When we choose the Riesz kernel or the Poisson kernel as the kernel, such centers are called a radial center or an illuminating center, respec…
Refines geometric center of mass analysis for Einstein field equations.
problem Analyzing the geometric center of mass of Willmore surfaces in initial data for Einstein field equations.
method Refined Lyapunov-Schmidt analysis to study geometric center of mass of area-constrained Willmore surfaces.
result The geometric center of mass agrees with the Hamiltonian center of mass under specific conditions.
New method quantifies variable importance in causal forests for treatment effect heterogeneity.
problem Lack of understanding how input variables affect treatment effect heterogeneity in causal forests.
method Developed a new importance variable algorithm for causal forests based on the drop and relearn principle.
result Shows how to handle forest retraining without a confounding variable and introduces a corrective term for confounders.
MAD-GAN detects anomalies in multivariate time series data using GANs.
problem Complex multivariate time series data requires better anomaly detection methods.
method Unsupervised anomaly detection using Generative Adversarial Networks (GANs).
result MAD-GAN effectively detects anomalies in real-world CPS systems.
Proposes an online method for high-dimensional streaming data.
problem Increasing variable dimensions with sample size in online kernel sliced inverse regression.
method Introduces approximate linear dependence condition and dictionary variable sets to address the problem. Transforms into online generalized eigen-decomposition problem and uses stochastic optimization for updates.
result Achieves close performance to batch processing kernel sliced inverse regression.
Let f:M->M be a partially hyperbolic diffeomorphism such that all of its center leaves are compact. We prove that Sullivan's example of a circle foliation that has arbitrary long leaves cannot be the center foliation of f. This is proved by thorough study of the accessible boundaries of the center-stable and the center…
We investigate centers of a body (the closure of a bounded open set) defined as maximum points of potentials. In particular, we study centers defined by the Riesz potential and by Poisson's integral. These centers, in general, depend on parameters and move with respect to the parameters. We give a necessary and suffici…
A new method for brain tissue segmentation across medical centers using a smoothness prior.
problem Tissue segmentation challenges due to center-specific acquisition protocols.
method Developed a smoothness prior that is fit to segmentations from another medical center, integrated into an unsupervised Bayesian model.
result Segmentations are similarly smooth across centers, improving generalization.
Proposes CGA model for logical queries over KGs.
problem Handling logical queries over incomplete KGs with unequal query path contributions.
method Multi-head graph attention with initial neighborhood aggregation for center node prediction.
result CGA model outperforms baselines on DB18, WikiGeo19, and Bio datasets.
In this paper are determined the principal curvatures and principal curvature lines on canal surfaces which are the envelopes of families of spheres with variable radius and centers moving along a closed regular curve in R^3. By means of a connection of the differential equations for these curvature lines and real Ricc…
We prove the existence of a center, or continuous selection of a point, in the relative interior of C1 embedded k-disks in Riemannian n-manifolds. If k≤3 the center can be made equivariant with respect to the isometries of the manifold, and under mild assumptions the same holds for k=4=n. By contrast, for…
Improves data normality with robust transformations.
problem Skewed data distribution.
method Modified Box-Cox and Yeo-Johnson transformations with robust parameter estimation.
result Transformed data approximates normality in the center with outliers.
Combines deep learning with constraints for better image generation.
problem Generating natural images with deep learning.
method Expectation-maximization framework with handcrafted constraints.
result Improved image generation using neural networks and constraints.
The paper quantizes concatenated noisy vectors to a common cluster center, improving performance over naive methods.
problem Clustering concatenated noisy vectors from multiple sources.
method Asymptotic analysis of weighted sum of distances to a common cluster center.
result The clustering approach outperforms naive methods in terms of average distortion.
Research examines GMIB and reset options in variable annuities.
problem Understanding the value and rationality of GMIB and reset options.
method Exploration of various parameters affecting GMIB value and calculation of critical future interest rates for reset option rationality.
result Insight into how future market performance and interest rates influence policyholder and insurer actions.
In this study, we investigate the locus of the centers of the Meusnier spheres. Just as focal curve is the locus of the centers of the osculating spheres, we investigate the geometrical interpretation on the locus of the centers of the Meusnier spheres. We proved that if the curve is a principal line, the locus of the …
Cauchy used infinitesimals in differential geometry and integral geometry.
problem Applying infinitesimals in differential and integral geometry.
method Using infinitesimals as numbers in differential and integral geometry.
result Valid application of infinitesimals in geometric probability, differential geometry, elasticity, and Dirac delta functions.
Categorical regressor variables are usually handled by introducing a set of indicator variables, and imposing a linear constraint to ensure identifiability in the presence of an intercept, or equivalently, using one of various coding schemes. As proposed in Yuan and Lin [J. R. Statist. Soc. B, 68 (2006), 49-67], the gr…
The determination of cluster centers generally depends on the scale that we use to analyze the data to be clustered. Inappropriate scale usually leads to unreasonable cluster centers and thus unreasonable results. In this study, we first consider the similarity of elements in the data as the connectivity of nodes in an…
Absolutely partially hyperbolic surface endomorphisms have a coherent center foliation.
problem Understanding the dynamics of absolutely partially hyperbolic surface endomorphisms.
method Showed the existence of a center foliation and leaf conjugacy to the linearization.
result Absolutely partially hyperbolic surface endomorphisms have a dynamically coherent center foliation.
Introduces Quantum Data Center for quantum era benefits.
problem No specific problem stated; focuses on future potential.
method Combines QRAM and quantum networks.
result QDC offers efficiency, security, and precision.
This research proposes a new distance metric using Isolation Forests.
problem Approximating spatial distance between data points.
method Isolation Forests for outlier detection, transforming separation depth into a distance metric.
result The method produces a distance metric invariant to variable scales and capable of handling non-linear relationships.
Unified theory linking atom-centered and message-passing models for molecular properties.
problem Combining atom-centered and message-passing models for accurate molecular property prediction.
method Generalizing ACDC framework to include multi-centered information, providing a complete linear basis for regression.
result Unified understanding of atom-centered and message-passing models, providing a coherent foundation.
Characterizes G2-structures on Lie algebras with non-trivial center.
problem Classifying Lie algebras with G2-structures.
method Analyzing Lie algebras with non-trivial center, using contactization and symplectic properties.
result Six unimodular Lie algebras with non-trivial center admit closed G2-structures.
A new method for optimizing functions with both categorical and continuous variables.
problem Optimizing functions with mixed categorical and continuous variables.
method Formulates as a multi-armed bandit problem, uses Thompson sampling, extends to batch BO.
result Proves sub-linear regret bounds and demonstrates effectiveness on various benchmarks.
Paper introduces new center of mass for flat manifolds.
problem Defining center of mass for asymptotically flat manifolds.
method Using double forms of Kulkarni and Labbi to prove existence and well-definedness.
result Existence and well-definedness of the Gauss-Bonnet-Chern center of mass.
Simplified proof of mass center system's uniqueness and generalized Pappus' theorem across Euclidean, spherical, and hyperbolic geometries.
problem Proving the uniqueness of the mass center system in non-Euclidean geometries and deriving a generalized Pappus' theorem.
method Revisiting and simplifying G.A. Galperin's proof, extending the mass center system to manifolds, and deriving a generalized Pappus' theorem.
result Unified and simpler proofs for Pappus' theorem in Euclidean, spherical, and hyperbolic geometries.
Improved clustering algorithm with random center count.
problem Worst-case approximation ratio of k-means++. method Randomizes the number of centers in k-means++. result An O(1)-approximation with constant probability.