yusuke1997/dblp-id-title huggingface dataset:
A Hierarchical Multilevel Markov Chain Monte Carlo Algorithm with Applications to Uncertainty Quantification in Subsurface Flow
Efficient Kriging Using Interleaved Lattice-Based Designs with Low Fill and High Separation Distance Properties
Statistical Treatment of Inverse Problems Constrained by Differential Equations-Based Models with Stochastic Terms
Context-Aware Surrogate Modeling for Balancing Approximation and Sampling Costs in Multifidelity Importance Sampling and Bayesian Inverse Problems
Comparing Scale Parameter Estimators for Gaussian Process Interpolation with the Brownian Motion Prior: Leave-One-Out Cross Validation and Maximum Likelihood
Certified Dimension Reduction for Bayesian Updating with the Cross-Entropy Method
A Sequential Design Approach for Calibrating Dynamic Computer Simulators
Accurate Computation of Conditional Expectation for Highly Nonlinear Problems
Error Decomposition and Adaptivity for Response Surface Approximations from PDEs with Parametric Uncertainty
Stability of Gibbs Posteriors from the Wasserstein Loss for Bayesian Full Waveform Inversion
Subsampled Gauss Quadrature Nodes for Estimating Polynomial Chaos Expansions
Model Uncertainty and Correctability for Directed Graphical Models
Reduced-Order Modeling with Time-Dependent Bases for PDEs with Stochastic Boundary Conditions
Stochastic Basis Adaptation and Spatial Domain Decomposition for Partial Differential Equations with Random Coefficients
Smoothing and Filtering with a Class of Outer Measures
Error Estimate of a Quasi-Monte Carlo Time-Splitting Pseudospectral Method for Nonlinear Schrödinger Equation with Random Potentials
Sequential Design of Computer Experiments for the Solution of Bayesian Inverse Problems
Asymptotic Properties of Probability Measure Estimators in a Nonparametric Model
Harmonizable Nonstationary Processes
Large Deviation Theory-based Adaptive Importance Sampling for Rare Events in High Dimensions
Assessing Stochastic Algorithms for Large Scale Nonlinear Least Squares Problems Using Extremal Probabilities of Linear Combinations of Gamma Random Variables
Bernstein-von Mises Theorems and Uncertainty Quantification for Linear Inverse Problems
Optimal Partition in Terms of Independent Random Vectors of Any Non-Gaussian Vector Defined by a Set of Realizations
Targeted Adaptive Design
A Hybrid Adaptive MCMC Algorithm in Function Spaces
Stochastic Conditioning of Matrix Functions
Sensitivity Analysis of Quasi-Stationary Distributions (QSDs) of Mass-Action Systems
Bayesian Emulation of Grey-Box Multimodel Ensembles Exploiting Known Interior Structure
Low-Rank Independence Samplers in Hierarchical Bayesian Inverse Problems
On the Well-posedness of Bayesian Inverse Problems
Modified Active Subspaces Using the Average of Gradients
Random Forward Models and Log-Likelihoods in Bayesian Inverse Problems
Robustness of the Sobol' Indices to Marginal Distribution Uncertainty
Consistency Analysis for Massively Inconsistent Datasets in Bound-to-Bound Data Collaboration
Emulating Complex Dynamical Simulators with Random Fourier Features
Adaptive Operator Learning for Infinite-Dimensional Bayesian Inverse Problems
A Flexible Uncertainty Propagation Framework for General Multiphysics Systems
On the Instability Issue of Gradient-Enhanced Gaussian Process Emulators for Computer Experiments
Computing Statistical Moments Via Tensorization of Polynomial Chaos Expansions
Stabilized Weighted Reduced Basis Methods for Parametrized Advection Dominated Problems with Random Inputs
Hierarchical Emulation: A Method for Modeling and Comparing Nested Simulators
Spectral Convergence of a Semi-discretized Numerical System for the Spatially Homogeneous Boltzmann Equation with Uncertainties
A Shapley-Owen Index for Interaction Quantification
A Posteriori Error and Optimal Reduced Basis for Stochastic Processes Defined by a Finite Set of Realizations
Uncertainty Quantification for Low-Frequency, Time-Harmonic Maxwell Equations with Stochastic Conductivity Models
Ensemble Transport Adaptive Importance Sampling
Multilevel Delayed Acceptance MCMC
A Simple, Bias-free Approximation of Covariance Functions by the Multilevel Monte Carlo Method Having Nearly Optimal Complexity
Sampling Parameters of Ordinary Differential Equations with Constrained Langevin Dynamics
Complexity Reduction in Many Particle Systems with Random Initial Data
Polynomial Chaos Expansion of a Multimodal Random Vector
Representing Model Discrepancy in Bound-to-Bound Data Collaboration
Decomposing Functional Model Inputs for Variance-Based Sensitivity Analysis
The Ensemble Kalman Filter for Rare Event Estimation
On Multilevel Best Linear Unbiased Estimators
Computer Emulation with Nonstationary Gaussian Processes
Analysis of an Asymptotic Preserving Scheme for Stochastic Linear Kinetic Equations in the Diffusion Limit
Sequential Implicit Sampling Methods for Bayesian Inverse Problems
Varying Coefficient Models and Design Choice for Bayes Linear Emulation of Complex Computer Models with Limited Model Evaluations
Flexible Correlation Structure for Accurate Prediction and Uncertainty Quantification in Bayesian Gaussian Process Emulation of a Computer Model
The Helmholtz Equation in Random Media: Well-Posedness and A Priori Bounds
Fokker-Planck Particle Systems for Bayesian Inference: Computational Approaches
Predictive Algorithm for Detection of Microcracks from Macroscale Observables
A Geometric Approach to the Transport of Discontinuous Densities
Gradient-Adjusted Underdamped Langevin Dynamics for Sampling
Computationally Efficient Sampling Methods for Sparsity Promoting Hierarchical Bayesian Models
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems Using Deep Neural Networks
A Framework for Multi-Model Ensembling
Wavelet-Based Density Estimation for Persistent Homology
Higher-Order Quasi-Monte Carlo for Bayesian Shape Inversion
Low-dimensional Subspace Regularization through Structured Tensor Priors
Multilevel Nested Simulation for Efficient Risk Estimation
Statistical Finite Elements via Interacting Particle Langevin Dynamics
Including a Nugget Effect in Lifted Brownian Covariance Models
Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark
Uncertainty Quantification in Machine Learning Based Segmentation: A Post-Hoc Approach for Left Ventricle Volume Estimation in MRI
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format
Uncertainty Quantification for Markov Processes via Variational Principles and Functional Inequalities
Multifidelity Monte Carlo Estimation with Adaptive Low-Fidelity Models
A Higher Order Perturbation Approach for Electromagnetic Scattering Problems on Random Domains
Are Minimizers of the Onsager-Machlup Functional Strong Posterior Modes?
Modeling and Predicting Chaotic Circuit Data
Intermediate Variable Emulation: Using Internal Processes in Simulators to Build More Informative Emulators
Quantifying and Managing Uncertainty in Piecewise-Deterministic Markov Processes
An Inverse Source Problem for the Stochastic Multiterm Time-Fractional Diffusion-Wave Equation
Mixed Finite Elements for Spatial Regression with PDE Penalization
Diagnostics-Driven Nonstationary Emulators Using Kernel Mixtures
An Empirical Interpolation and Model-Variance Reduction Method for Computing Statistical Outputs of Parametrized Stochastic Partial Differential Equations
Gaussian Processes with Input Location Error and Applications to the Composite Parts Assembly Process
Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators under Mesh Refinement
Calculation of Epidemic First Passage and Peak Time Probability Distributions
A Lagged Particle Filter for Stable Filtering of Certain High-Dimensional State-Space Models
Quantifying Uncertainties on Excursion Sets Under a Gaussian Random Field Prior
Random Fourier Features Based Gaussian Process Models for Stochastic Simulations
When Is a Model Good Enough? Deriving the Expected Value of Model Improvement via Specifying Internal Model Discrepancies
Bayesian Optimization of Expected Quadratic Loss for Multiresponse Computer Experiments with Internal Noise
Objective Frequentist Uncertainty Quantification for Atmospheric \(\mathrm{CO}_2\) Retrievals
A Comparison of Closures for Stochastic Advection-Diffusion Equations
Multiple Closed Curve Modeling with Uncertainty Quantification for Shape Analysis
On the Deep Active-Subspace Method
Generalized Bayesian MARS: Tools for Stochastic Computer Model Emulation
Active Learning of Tree Tensor Networks using Optimal Least Squares
Stable Approximation Schemes for Optimal Filters
Optimal Control under Uncertainty with Joint Chance State Constraints: Almost-Everywhere Bounds, Variance Reduction, and Application to (Bi)linear Elliptic PDEs
Uncertainty Propagation; Intrusive Kinetic Formulations of Scalar Conservation Laws
Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis-Hastings
Using Proper Divergence Functions to Evaluate Climate Models
Global Sensitivity Analysis and Wasserstein Spaces
Convergence of the Square Root Ensemble Kalman Filter in the Large Ensemble Limit
Dimension Reduction in Statistical Estimation of Partially Observed Multiscale Processes
Effective Generation of Compressed Stationary Gaussian Fields
Spectral Methods for Multiscale Stochastic Differential Equations
Bayesian Analysis of ODEs: Solver Optimal Accuracy and Bayes Factors
Global Sensitivity Analysis for Optimization with Variable Selection
Sparse Online Variational Bayesian Regression
Numerical Continuation and SPDE Stability for the 2D Cubic-Quintic Allen-Cahn Equation
Multilevel Monte Carlo Covariance Estimation for the Computation of Sobol' Indices
Learning Inducing Points and Uncertainty on Molecular Data by Scalable Variational Gaussian Processes
Cubature Formulas for Multisymmetric Functions and Applications to Stochastic Partial Differential Equations
Proportional Marginal Effects for Global Sensitivity Analysis
Message from the Editors
On the Mean Field Theory of Ensemble Kalman Filters for SPDEs
Reproducing Kernel Hilbert Spaces, Polynomials, and the Classical Moment Problem
A Quasi-Monte Carlo Method for Optimal Control Under Uncertainty
A Novel Methodology of Stochastic Short Term Forecasting of Cloud Boundaries
Multilevel Monte Carlo Approximation of Distribution Functions and Densities
A Bramble-Pasciak Conjugate Gradient Method for Discrete Stokes Equations with Random Viscosity
Rigorous Analysis for Efficient Statistically Accurate Algorithms for Solving Fokker-Planck Equations in Large Dimensions
Leveraging Joint Sparsity in Hierarchical Bayesian Learning
Approximation of Limit State Surfaces in Monotonic Monte Carlo Settings, with Applications to Classification
Ensemble Kalman Filters with Resampling
Uncertainty Quantification for Markov Random Fields
Efficient White Noise Sampling and Coupling for Multilevel Monte Carlo with Nonnested Meshes
A Goal-Oriented Reduced Basis Methods-Accelerated Generalized Polynomial Chaos Algorithm
Uncertainty Quantification and Experimental Design for Large-Scale Linear Inverse Problems under Gaussian Process Priors
Convergence of Gaussian Process Regression with Estimated Hyper-Parameters and Applications in Bayesian Inverse Problems
Efficient History Matching of a High Dimensional Individual-Based HIV Transmission Model
Generalized Sparse Bayesian Learning and Application to Image Reconstruction
Local Equilibration Error Estimators for Guaranteed Error Control in Adaptive Stochastic Higher-Order Galerkin Finite Element Methods
Gaussian Approximations for Probability Measures on R d
Density Estimation by Randomized Quasi-Monte Carlo
Nonparametric Estimation for Independent and Identically Distributed Stochastic Differential Equations with Space-Time Dependent Coefficients
Robust Bounds on Risk-Sensitive Functionals via Rényi Divergence
Greedy Algorithms for Optimal Measurements Selection in State Estimation Using Reduced Models
Statistical Inference for Conservation Law McKean-Vlasov SDEs via Deep Neural Networks
Adaptive Design for Contour Estimation from Computer Experiments with Quantitative and Qualitative Inputs
Multifidelity Approximate Bayesian Computation with Sequential Monte Carlo Parameter Sampling
Superfloe Parameterization with Physics Constraints for Uncertainty Quantification of Sea Ice Floes
Dimension-Independent MCMC Sampling for Inverse Problems with Non-Gaussian Priors
Multiobjective Optimization Using Expected Quantile Improvement for Decision Making in Disease Outbreaks
Uncertainty Quantification for Spectral Fractional Diffusion: Sparsity Analysis of Parametric Solutions
Sampling-based Spotlight SAR Image Reconstruction from Phase History Data for Speckle Reduction and Uncertainty Quantification
Sequential Design for Ranking Response Surfaces
Stability Preservation in Stochastic Galerkin Projections of Dynamical Systems
Monotone Emulation of Computer Experiments
Multilevel Monte Carlo Finite Difference Methods for Fractional Conservation Laws with Random Data
A Newton-Galerkin Method for Fluid Flow Exhibiting Uncertain Periodic Dynamics
Bayesian Spatial Inversion and Conjugate Selection Gaussian Prior Models
Sensitivity Analysis Based on Cramér-von Mises Distance
Non-convergence to Global Minimizers for Adam and Stochastic Gradient Descent Optimization and Constructions of Local Minimizers in the Training of Artificial Neural Networks
On Nonintrusive Uncertainty Quantification and Surrogate Model Construction in Particle Accelerator Modeling
Shapley Effects for Global Sensitivity Analysis: Theory and Computation
The ROMES Method for Statistical Modeling of Reduced-Order-Model Error
Scalable Bayesian Physics-Informed Kolmogorov-Arnold Networks
Selecting Reduced Models in the Cross-Entropy Method
Path-Space Information Bounds for Uncertainty Quantification and Sensitivity Analysis of Stochastic Dynamics
Equispaced Fourier Representations for Efficient Gaussian Process Regression from a Billion Data Points
An Optimal Solver for Linear Systems Arising from Stochastic FEM Approximation of Diffusion Equations with Random Coefficients
Two-Level a Posteriori Error Estimation for Adaptive Multilevel Stochastic Galerkin Finite Element Method
Domain Uncertainty Quantification in Computational Electromagnetics
Stratification as a General Variance Reduction Method for Markov Chain Monte Carlo
Well-Posed Bayesian Inverse Problems with Infinitely Divisible and Heavy-Tailed Prior Measures
Surrogate-Based Global Sensitivity Analysis with Statistical Guarantees via Floodgate
Continuous Level Monte Carlo and Sample-Adaptive Model Hierarchies
Efficient Bayesian Experimentation Using an Expected Information Gain Lower Bound
Quasi-Monte Carlo Integration for Feedback Control Under Uncertainty
Bayesian Subset Simulation
Information Geometry for Approximate Bayesian Computation
Robust Kalman and Bayesian Set-Valued Filtering and Model Validation for Linear Stochastic Systems
Adaptive A-Optimal Experimental Design for Linear Dynamical Systems
Stochastic Resin Transfer Molding Process
Rising Above Chaotic Likelihoods
A Defensive Marginal Particle Filtering Method for Data Assimilation
A Multilevel Stochastic Collocation Algorithm for Optimization of PDEs with Uncertain Coefficients
Lattice Boltzmann Method for Stochastic Convection-Diffusion Equations
On Unbiased Estimation for Discretized Models
Dynamical Polynomial Chaos Expansions and Long Time Evolution of Differential Equations with Random Forcing
Block Preconditioning of Stochastic Galerkin Problems: New Two-sided Guaranteed Spectral Bounds
Sparse Grid Approximation of Nonlinear SPDEs: The Landau-Lifshitz-Gilbert Equation
Lagrangian Uncertainty Quantification and Information Inequalities for Stochastic Flows
Low-Rank Eigenvector Compression of Posterior Covariance Matrices for Linear Gaussian Inverse Problems
Nonparametric Posterior Learning for Emission Tomography
Multilevel Monte Carlo Metamodeling for Variance Function Estimation
A Trade-Off Between Explorations and Repetitions for Estimators of Two Global Sensitivity Indices in Stochastic Models Induced by Probability Measures
A Framework for Controlling Sources of Inaccuracy in Gaussian Process Emulation of Deterministic Computer Experiments
Risk-Adapted Optimal Experimental Design
Precision and Cholesky Factor Estimation for Gaussian Processes
Cellular Probabilistic Automata - A Novel Method for Uncertainty Propagation
Analysis of the Gibbs Sampler for Hierarchical Inverse Problems
Covariate-Informed Bifidelity Bias Correction of Distributional Output
Multifidelity Bayesian Experimental Design to Quantify Rare-Event Statistics
Quantifying Spatio-Temporal Boundary Condition Uncertainty for the North American Deglaciation
Finite Element Representations of Gaussian Processes: Balancing Numerical and Statistical Accuracy
Deep Learning in High Dimension: Neural Network Expression Rates for Analytic Functions in \(\pmb{L^2(\mathbb{R}^d,\gamma_d)}\)
Bayesian Quadrature, Energy Minimization, and Space-Filling Design
A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design
Ensemble Markov Chain Monte Carlo with Teleporting Walkers
Mean Field Games for Controlling Coherent Structures in Nonlinear Fluid Systems
Ensemble-Based Gradient Inference for Particle Methods in Optimization and Sampling
Differential Equation-Constrained Optimization with Stochasticity
Lurking Variable Detection via Dimensional Analysis
Certified Multifidelity Zeroth-Order Optimization
Global Sensitivity Analysis for Statistical Model Parameters
Uniform Regularity for Linear Kinetic Equations with Random Input Based on Hypocoercivity
Allocation Strategies for High Fidelity Models in the Multifidelity Regime
Linear Parabolic Problems in Random Moving Domains
Simulation-Based Uncertainty Quantification for Estimating Atmospheric CO 2 from Satellite Data
Robust Information Divergences for Model-Form Uncertainty Arising from Sparse Data in Random PDE
On the Asymptotic Normality of Adaptive Multilevel Splitting
Multilevel Accelerated Quadrature for PDEs with Log-Normally Distributed Diffusion Coefficient
Parallel Local Approximation MCMC for Expensive Models
Nonparametric lack-of-fit tests for parametric mean-regression models with censored data
Monitoring procedures for strict stationarity based on the multivariate characteristic function
Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data
Joint graphical lasso with regularized aggregation
Tree Pólya Splitting distributions for multivariate count data
Adaptive group bridge selection in the semiparametric accelerated failure time model
Sparse model identification and learning for ultra-high-dimensional additive partially linear models
Trimmed regions induced by parameters of a probability
Quantifying directed dependence via dimension reduction
The determinants of cumulative endogeneity bias in multivariate analysis
Likelihood ratio tests for positivity in polynomial regressions
Extremes of scale mixtures of multivariate time series
Optimal Bayesian smoothing of functional observations over a large graph
Multivariate distributions and the moment problem
Measures of influence for the functional linear model with scalar response
Robust linear functional mixed models
On the degrees of freedom in shrinkage estimation
Bootstrap testing for discontinuities under long-range dependence
Bayesian hierarchical robust factor analysis models for partially observed sample-selection data
Testing the equality of a large number of means of functional data
Testing homogeneity in high dimensional data through random projections
Estimations for some functions of covariance matrix in high dimension under non-normality and its applications
Boundary kernels for adaptive density estimators on regions with irregular boundaries
Multivariate analysis of covariance with potentially singular covariance matrices and non-normal responses
Estimation of two high-dimensional covariance matrices and the spectrum of their ratio
Dense classes of multivariate extreme value distributions
Design-free estimation of integrated covariance matrices for high-frequency data
Local asymptotic normality in a stationary model for spatial extremes
Parametric estimation and tests through divergences and the duality technique
Correlation tests for high-dimensional data using extended cross-data-matrix methodology
Alteration detection of tensor dependence structure via sparsity-exploited reranking algorithm
On positive association of absolute-valued and squared multivariate Gaussians beyond MTP2
The extent of the maximum likelihood estimator for the extreme value index
Empirical and sequential empirical copula processes under serial dependence
A continuous spectral density for a random field of continuous-index
Change-point problems for multivariate time series using pseudo-observations
Central tolerance regions and reference regions for multivariate normal populations
Modeling spatial anisotropy via regression with partial differential regularization
Nonparametric variance function estimation with missing data
Robust network-based analysis of the associations between (epi)genetic measurements
Nonparametric estimation of the distribution of the autoregressive coefficient from panel random-coefficient AR(1) data
Empirical Hankel transforms and its applications to goodness-of-fit tests
Scale and curvature effects in principal geodesic analysis
Departure from normality of increasing-dimension martingales
A local spectral approach for assessing time series model misspecification
Penalized multiply robust estimation in high-order autoregressive processes with missing explanatory variables
A subspace estimator for fixed rank perturbations of large random matrices
Joint asymptotics for estimating the fractal indices of bivariate Gaussian processes
The analysis of distance of grouped data with categorical variables: Categorical canonical variate analysis
On singular values of large dimensional lag-τ sample auto-correlation matrices
Empirical processes for infinite variance autoregressive models
Nonparametric comparison of regression functions
Asymptotic distributions of maxima of complete and incomplete samples from multivariate stationary Gaussian sequences
Estimation of multiple networks with common structures in heterogeneous subgroups
Asymptotically efficient two-sample rank tests for modal directions on spheres
Consistent estimation of a joint model for multivariate longitudinal and survival data with latent variables
On the estimation of population sizes in capture-recapture experiments
A note on the regularity of optimal-transport-based center-outward distribution and quantile functions
Penalized Whittle likelihood for spatial data
Canonical correlation analysis for elliptical copulas
Scale and shape mixtures of matrix variate extended skew normal distributions
Shrinkage estimation in spatial autoregressive model
Empirical likelihood for linear transformation models with interval-censored failure time data
Simple models for multivariate regular variation and the Hüsler-Reiß Pareto distribution
Bootstrap confidence bands and partial linear quantile regression
Stochastic orderings for elliptical random vectors
ℓ2, 0-norm based selection and estimation for multivariate generalized linear models
Subgroup effect quantile regression with high dimensional missing panel data
On high dimensional two-sample tests based on nearest neighbors
Conditional multivariate distributions of phase-type for a finite mixture of Markov jump processes given observations of sample path
A bivariate Gompertz-Makeham life distribution
The dictionary approach for spherical deconvolution
Multivariate and functional robust fusion methods for structured Big Data
Pattern recognition based on canonical correlations in a high dimension low sample size context
Bayesian joint inference for multiple directed acyclic graphs
On the use of coordinate-free matrix calculus
Model selection in linear mixed effect models
A contribution to the visualisation of three-way arrays
Semi-parametric copula-based models under non-stationarity
Quadratic prediction and quadratic sufficiency in finite populations
On the conjecture of Kochar and Korwar