yusuke1997/dblp-id-title huggingface dataset:
Adaptive Reduced-Order Model Construction for Conditional Value-at-Risk Estimation
A Gradient-based and Determinant-free Framework for Fully Bayesian Gaussian Process Regression
Warped Gaussian Processes and Derivative-Based Sequential Designs for Functions with Heterogeneous Variations
Approximate Solutions of Inverse Problems for Nonlinear Space Fractional Diffusion Equations with Randomly Perturbed Data
Automating Emulator Construction for Geophysical Hazard Maps
An Iterative Ensemble Kalman Smoother in Presence of Additive Model Error
Empirical Bayesian Inference Using a Support Informed Prior
Approximate Solutions of Lagrange Multipliers for Information-Theoretic Random Field Models
An Analysis of Infinite Dimensional Bayesian Inverse Shape Acoustic Scattering and Its Numerical Approximation
A Comparative Study of Polynomial-Type Chaos Expansions for Indicator Functions
Reliable Error Estimates for Optimal Control of Linear Elliptic PDEs with Random Inputs
Sequential Design of Experiment for Sparse Polynomial Chaos Expansions
Stochastic Least-Squares Petrov-Galerkin Method for Parameterized Linear Systems
Statistical Finite Elements via Langevin Dynamics
Conditional Optimal Transport on Function Spaces
Dimension Free Nonasymptotic Bounds on the Accuracy of High-Dimensional Laplace Approximation
Bayesian Inference via Filtering Equations for Ultrahigh Frequency Data (II): Model Selection
Joint Online Parameter Estimation and Optimal Sensor Placement for the Partially Observed Stochastic Advection-Diffusion Equation
Rare-Event Simulation for the Stochastic Korteweg-de Vries Equation
Corrigendum: "Existence and Optimality Conditions for Risk-Averse PDE-Constrained Optimization"
A Combined Probabilistic/Nonprobabilistic Decision Analysis for Contaminant Remediation
A Multifidelity Estimator of the Expected Information Gain for Bayesian Optimal Experimental Design
Hierarchical Shrinkage Gaussian Processes: Applications to Computer Code Emulation and Dynamical System Recovery
Uncertainty Quantification of Inclusion Boundaries in the Context of X-Ray Tomography
Instances of Computational Optimal Recovery: Dealing with Observation Errors
A Method of Moments Estimator for Interacting Particle Systems and their Mean Field Limit
On the Improved Rates of Convergence for Matérn-Type Kernel Ridge Regression with Application to Calibration of Computer Models
Linked Gaussian Process Emulation for Systems of Computer Models Using Matérn Kernels and Adaptive Design
Convergence Rates for Penalized Least Squares Estimators in PDE Constrained Regression Problems
Predicting Dynamic Trends of the Atlantic Meridional Overturning Circulation for Transient and Stochastic Forcing Effects
Sequential Monte Carlo with Highly Informative Observations
Corrigendum: Quasi-Monte Carlo Finite Element Analysis for Wave Propagation in Heterogeneous Random Media
Nonintrusive Polynomial Chaos Expansions for Sensitivity Analysis in Stochastic Differential Equations
Continuum Covariance Propagation for Understanding Variance Loss in Advective Systems
Evaluating Forecasts for High-Impact Events Using Transformed Kernel Scores
Experimental Design for Partially Observed Markov Decision Processes
Analysis of the Zero Relaxation Limit of Hyperbolic Balance Laws with Random Initial Data
A Spline Dimensional Decomposition for Uncertainty Quantification in High Dimensions
Prediction Uncertainties beyond the Range of Experience: A Case Study in Inertial Confinement Fusion Implosion Experiments
Bayesian Parameter Identification in Cahn-Hilliard Models for Biological Growth
Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration
Hierarchical Tensor Approximation of Output Quantities of Parameter-Dependent PDEs
Coarse-Grid Sampling Interpolatory Methods for Approximating Gaussian Random Fields
Modeling Uncertainty in Climate Using Ensembles of Regional and Global Climate Models and Multiple Observation-Based Data Sets
Optimal Adaptation for Early Stopping in Statistical Inverse Problems
An Efficient and Accurate Method for the Identification of the Most Influential Random Parameters Appearing in the Input Data for PDEs
Kernel Methods for Bayesian Elliptic Inverse Problems on Manifolds
An Adaptive Multilevel Monte Carlo Method with Stochastic Bounds for Quantities of Interest with Uncertain Data
Convergence Rates for Learning Linear Operators from Noisy Data
Parameter Selection in Gaussian Process Interpolation: An Empirical Study of Selection Criteria
Covariance-Free Bifidelity Control Variates Importance Sampling for Rare Event Reliability Analysis
A Multilevel Stochastic Collocation Method for Schrödinger Equations with a Random Potential
An Inverse Random Source Problem for the Biharmonic Wave Equation
PDE-Constrained Optimal Control Problems with Uncertain Parameters using SAGA
A Convex Optimization Framework for the Inverse Problem of Identifying a Random Parameter in a Stochastic Partial Differential Equation
Solving Log-Transformed Random Diffusion Problems by Stochastic Galerkin Mixed Finite Element Methods
Entropy-Based Burn-in Time Analysis and Ranking for (A)MCMC Algorithms in High Dimension
An Adaptive Hierarchical Ensemble Kalman Filter with Reduced Basis Models
An MCMC Algorithm for Parameter Estimation in Signals with Hidden Intermittent Instability
Antithetic Multilevel Methods for Elliptic and Hypoelliptic Diffusions with Applications
Worst-Case Learning under a Multifidelity Model
A Practical Method to Estimate Information Content in the Context of 4D-Var Data Assimilation
Scaled Vecchia Approximation for Fast Computer-Model Emulation
A Bayesian Approach for Global Sensitivity Analysis of (Multifidelity) Computer Codes
Low-Rank Tensor Approximation for High-Order Correlation Functions of Gaussian Random Fields
Reduced Basis Methods for Parameterized Partial Differential Equations with Stochastic Influences Using the Karhunen-Loève Expansion
Universal Prediction Distribution for Surrogate Models
Transport Map Accelerated Adaptive Importance Sampling, and Application to Inverse Problems Arising from Multiscale Stochastic Reaction Networks
Emulation of Multivariate Simulators Using Thin-Plate Splines with Application to Atmospheric Dispersion
Towards Practical Large-Scale Randomized Iterative Least Squares Solvers through Uncertainty Quantification
Multifidelity Approximate Bayesian Computation
On Disturbance State-Space Models and the Particle Marginal Metropolis-Hastings Sampler
Robust Optimization of PDEs with Random Coefficients Using a Multilevel Monte Carlo Method
Multilevel Monte Carlo Estimation of the Expected Value of Sample Information
Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models Using Markov Chain Monte Carlo
Probability and Cumulative Density Function Methods for the Stochastic Advection-Reaction Equation
Bayesian Inference of an Uncertain Generalized Diffusion Operator
Finite-Dimensional Gaussian Approximation with Linear Inequality Constraints
Multilevel Estimation of Expected Exit Times and Other Functionals of Stopped Diffusions
Probabilistic Density Function Method for Stochastic ODEs of Power Systems with Uncertain Power Input
A Posteriori Error Estimates for Mixed Finite Element and Finite Volume Methods for Parabolic Problems Coupled through a Boundary
Domain Uncertainty Quantification for the Lippmann-Schwinger Volume Integral Equation
A Theoretical Framework for Calibration in Computer Models: Parametrization, Estimation and Convergence Properties
Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation
Mini-Minimax Uncertainty Quantification for Emulators
Analysis of Nested Multilevel Monte Carlo Using Approximate Normal Random Variables
The f-Sensitivity Index
Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification
Optimal Explicit Stabilized Integrator of Weak Order 1 for Stiff and Ergodic Stochastic Differential Equations
Ensemble Approximate Control Variate Estimators: Applications to MultiFidelity Importance Sampling
Gaussian Process-Based Dimension Reduction for Goal-Oriented Sequential Design
Parameter Inference Based on Gaussian Processes Informed by Nonlinear Partial Differential Equations
Robustness of Solutions of the Inverse Problem for Linear Dynamical Systems with Uncertain Data
Characterizing Impacts of Model Uncertainties in Quantitative Photoacoustics
Efficiency of the Girsanov Transformation Approach for Parametric Sensitivity Analysis of Stochastic Chemical Kinetics
HMC and Underdamped Langevin United in the Unadjusted Convex Smooth Case
Reduced Basis Collocation Methods for Partial Differential Equations with Random Coefficients
Nonlinear Reduced Models for State and Parameter Estimation
Impact of Data Assimilation on Cost-Accuracy Tradeoff in Multifidelity Models
Shape Optimization under Constraints on the Probability of a Quadratic Functional to Exceed a Given Threshold
Tensor Train Based Sampling Algorithms for Approximating Regularized Wasserstein Proximal Operators
Quasi-Monte Carlo Finite Element Analysis for Wave Propagation in Heterogeneous Random Media
Data Assimilation in Reduced Modeling
Quantification of Airfoil Geometry-Induced Aerodynamic Uncertainties - Comparison of Approaches
Dimension Reduction via Gaussian Ridge Functions
Stochastic Modeling and Regularity of the Nonlinear Elliptic curl-curl Equation
Model Error Estimation Using the Expectation Maximization Algorithm and a Particle Flow Filter
Adaptive Importance Sampling Based on Fault Tree Analysis for Piecewise Deterministic Markov Process
Beyond the Model Limit: Parameter Inference Across Scales
Generalizing Parallel Replica Dynamics: Trajectory Fragments, Asynchronous Computing, and PDMPs
Physics-Based Kriging Surrogates for a Class of Finite Element Codes
Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices
Fully Bayesian Inference for Latent Variable Gaussian Process Models
Comparison of Statistical and Deterministic Frameworks of Uncertainty Quantification
Block-Diagonal Covariance Estimation and Application to the Shapley Effects in Sensitivity Analysis
Generalized Modes in Bayesian Inverse Problems
On the Asymptotical Regularization for Linear Inverse Problems in Presence of White Noise
A Hybrid Alternating Least Squares-TT-Cross Algorithm for Parametric PDEs
Scalable Method for Bayesian Experimental Design without Integrating over Posterior Distribution
Control Theory and Experimental Design in Diffusion Processes
Calibrating a Stochastic, Agent-Based Model Using Quantile-Based Emulation
Data-Driven Rules for Multidimensional Reflection Problems
Efficient Marginalization-Based MCMC Methods for Hierarchical Bayesian Inverse Problems
Fast Sampling in a Linear-Gaussian Inverse Problem
Selecting Parameters for Bayesian Calibration of a Process-Based Model: A Methodology Based on Canonical Correlation Analysis
ERRATUM: A Hierarchical Multilevel Markov Chain Monte Carlo Algorithm with Applications to Uncertainty Quantification in Subsurface Flow
Building Population-Informed Priors for Bayesian Inference Using Data-Consistent Stochastic Inversion
Gaussian Linear Approximation for the Estimation of the Shapley Effects
Formulating Natural Hazard Policies under Uncertainty
Nonparametric Uncertainty Quantification for Stochastic Gradient Flows
Convergence and Error Propagation Results on a Linear Iterative Unfolding Method
Variance Reduction for Dependent Sequences with Applications to Stochastic Gradient MCMC
Probabilistic Description of Extreme Events in Intermittently Unstable Dynamical Systems Excited by Correlated Stochastic Processes
Resource-Constrained Model Selection for Uncertainty Propagation and Data Assimilation
Gauss von Mises Distribution for Improved Uncertainty Realism in Space Situational Awareness
Theoretical Guarantees for the Statistical Finite Element Method
Analysis of Boundary Effects on PDE-Based Sampling of Whittle-Matérn Random Fields
Displacement Interpolation Using Monotone Rearrangement
Long-Time Asymptotics of the Filtering Distribution for Partially Observed Chaotic Dynamical Systems
Polynomial Chaos Expansion of Random Coefficients and the Solution of Stochastic Partial Differential Equations in the Tensor Train Format
Two Metropolis-Hastings Algorithms for Posterior Measures with Non-Gaussian Priors in Infinite Dimensions
Noise Level Free Regularization of General Linear Inverse Problems under Unconstrained White Noise
Computing Shapley Effects for Sensitivity Analysis
A Physics-Based Emulator for the Simulation of Geophysical Mass Flows
A Nonparametric Bayesian Framework for Uncertainty Quantification in Stochastic Simulation
A Multifidelity Quantile-Based Approach for Confidence Sets of Random Excursion Sets with Application to Ice-Sheet Dynamics
Maximum Likelihood Estimation for a Smooth Gaussian Random Field Model
A Stochastic Levenberg-Marquardt Method Using Random Models with Complexity Results
Fast Calibration for Computer Models with Massive Physical Observations
Consistency of Bayesian Inference for a Subdiffusion Equation
Sobol' Indices and Shapley Value
Existence, Uniqueness, and a Comparison of Nonintrusive Methods for the Stochastic Nonlinear Poisson-Boltzmann Equation
A Theoretical Framework of the Scaled Gaussian Stochastic Process in Prediction and Calibration
Asymptotic Analysis of Multilevel Best Linear Unbiased Estimators
Deep Neural Network Surrogates for Nonsmooth Quantities of Interest in Shape Uncertainty Quantification
A Posteriori Estimates for Backward SDEs
A Sparse Stochastic Collocation Technique for High-Frequency Wave Propagation with Uncertainty
Complete Deterministic Dynamics and Spectral Decomposition of the Linear Ensemble Kalman Inversion
Optimal Design for Estimation in Diffusion Processes from First Hitting Times
A Multilevel Stochastic Collocation Method for Partial Differential Equations with Random Input Data
Complex-Valued Signal Recovery Using a Generalized Bayesian LASSO
On the Importance of the Jacobian Determinant in Parameter Inference for Random Parameter and Random Measurement Error Models
A Continuation Method in Bayesian Inference
Sequential Bayesian Polynomial Chaos Model Selection for Estimation of Sensitivity Indices
Constructing Sampling Schemes via Coupling: Markov Semigroups and Optimal Transport
Ensemble Grouping Strategies for Embedded Stochastic Collocation Methods Applied to Anisotropic Diffusion Problems
Conditional-Value-at-Risk Estimation via Reduced-Order Models
Sequential Design with Mutual Information for Computer Experiments (MICE): Emulation of a Tsunami Model
APIK: Active Physics-Informed Kriging Model with Partial Differential Equations
Efficient Computation of Extreme Excursion Probabilities for Dynamical Systems through Rice's Formula
Compressed Principal Component Analysis of Non-Gaussian Vectors
Inverse Random Source Scattering Problems in Several Dimensions
Well-Posed Bayesian Inverse Problems: Priors with Exponential Tails
A Data-Driven Stochastic Method for Elliptic PDEs with Random Coefficients
Spectral Approximation of the IMSE Criterion for Optimal Designs in Kernel-Based Interpolation Models
Penalized Projected Kernel Calibration for Computer Models
Elastic Bayesian Model Calibration
\({p}\)-Multilevel Monte Carlo for Acoustic Scattering from Large Deviation Rough Random Surfaces
Grouped Orthogonal Arrays for Computer Experiments with Grouped Inputs
Metropolized Randomized Maximum Likelihood for Improved Sampling from Multimodal Distributions
Compressive Sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions
Failure Probability Estimation of Linear Time Varying Systems by Progressive Refinement of Reduced Order Models
Adjustments to Computer Models via Projected Kernel Calibration
Estimation of the Derivative-Based Global Sensitivity Measures Using a Gaussian Process Metamodel
A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations
Hybrid Sampling/Spectral Method for Solving Stochastic Coupled Problems
Inexact Methods for Symmetric Stochastic Eigenvalue Problems
Emulating Satellite Drag from Large Simulation Experiments
On the Generalized Langevin Equation for Simulated Annealing
A Semiautomatic Method for History Matching Using Sequential Monte Carlo
A Particle Filter for Stochastic Advection by Lie Transport: A Case Study for the Damped and Forced Incompressible Two-Dimensional Euler Equation
An Information Criterion for Choosing Observation Locations in Data Assimilation and Prediction
A Locally Adapted Reduced-Basis Method for Solving Risk-Averse PDE-Constrained Optimization Problems
Multilevel Sequential Monte Carlo with Dimension-Independent Likelihood-Informed Proposals
Erratum: A Comparison of Closures for Stochastic Advection-Diffusion Equations
Sampling Low-Fidelity Outputs for Estimation of High-Fidelity Density and Its Tails
Robust System Design with Limited Experimental Data and an Inexact Simulation Model
A Posteriori Error Estimates for the Solution of Variational Inverse Problems
Numerical Analysis of the Advection-Diffusion of a Solute in Porous Media with Uncertainty
Multilevel Monte Carlo Approximation of Functions
Parameter Estimation in an SPDE Model for Cell Repolarization
Sequential Monte Carlo Methods for High-Dimensional Inverse Problems: A Case Study for the Navier-Stokes Equations
Representing Model Inadequacy: A Stochastic Operator Approach
Functional ANOVA with Multiple Distributions: Implications for the Sensitivity Analysis of Computer Experiments
The Zero Problem: Gaussian Process Emulators for Range-Constrained Computer Models
Projected Wasserstein Gradient Descent for High-Dimensional Bayesian Inference
A Generalized ANOVA Dimensional Decomposition for Dependent Probability Measures
A Geometrical Method for Low-Dimensional Representations of Simulations
On Shapley Value for Measuring Importance of Dependent Inputs
Density Estimation in Uncertainty Propagation Problems Using a Surrogate Model
Monte Carlo Methods for the Neutron Transport Equation
Levenberg-Marquardt Methods Based on Probabilistic Gradient Models and Inexact Subproblem Solution, with Application to Data Assimilation
Sensitivity Analysis of Continuous Time Bayesian Network Reliability Models
Gaussian Process Regression on Nested Spaces
Robust A-Optimal Experimental Design for Sensor Placement in Bayesian Linear Inverse Problems
Conditional Sampling with Monotone GANs: From Generative Models to Likelihood-Free Inference
GAN-Based Priors for Quantifying Uncertainty in Supervised Learning
Discovering the Unknowns: A First Step
Finite-Dimensional Models for Response Analysis
Fuzzy-Stochastic Partial Differential Equations
Mean Exit Times and the Multilevel Monte Carlo Method
An Efficient, Globally Convergent Method for Optimization Under Uncertainty Using Adaptive Model Reduction and Sparse Grids
Robust Level-Set-Based Topology Optimization Under Uncertainties Using Anchored ANOVA Petrov-Galerkin Method
Quantifying Truncation-Related Uncertainties in Unsteady Fluid Dynamics Reduced Order Models
Evolving Bayesian Emulators for Structured Chaotic Time Series, with Application to Large Climate Models
Stochastic Galerkin Finite Element Method with Local Conductivity Basis for Electrical Impedance Tomography
The Algebraic Method in Quadrature for Uncertainty Quantification
Coupling Computer Models through Linking Their Statistical Emulators
Variational Multiscale Analysis: The Fine-Scale Green's Function for Stochastic Partial Differential Equations
Goal-Oriented Shapley Effects with Special Attention to the Quantile-Oriented Case
Locating Infinite Discontinuities in Computer Experiments
Accurate Solution of Bayesian Inverse Uncertainty Quantification Problems Combining Reduced Basis Methods and Reduction Error Models
Taylor Approximation for Chance Constrained Optimization Problems Governed by Partial Differential Equations with High-Dimensional Random Parameters
A Convergent Interacting Particle Method for Computing KPP Front Speeds in Random Flows
Optimal Approximation of the First-Order Corrector in Multiscale Stochastic Elliptic PDE
Accelerate Langevin Sampling with Birth-Death Process and Exploration Component
Uniform Error Bounds of the Ensemble Transform Kalman Filter for Chaotic Dynamics with Multiplicative Covariance Inflation
Optimization-Based Markov Chain Monte Carlo Methods for Nonlinear Hierarchical Statistical Inverse Problems
A Least-Squares Method for Sparse Low Rank Approximation of Multivariate Functions
Sparse Adaptive Tensor Galerkin Approximations of Stochastic PDE-Constrained Control Problems
Existence and Optimality Conditions for Risk-Averse PDE-Constrained Optimization
A Hybrid Ensemble Transform Particle Filter for Nonlinear and Spatially Extended Dynamical Systems
Uncertainty Quantification for Stochastic Approximation Limits Using Chaos Expansion
Uncertainty Quantification for Approximate p-Quantiles for Physical Models with Stochastic Inputs
Polynomial Chaos Expansions for the Stability Analysis of Uncertain Delay Differential Equations
Reproducing Kernel Hilbert Spaces for Parametric Partial Differential Equations
Efficient Reduced Basis Methods for Saddle Point Problems with Applications in Groundwater Flow
Gradient-Free Construction of Active Subspaces for Dimension Reduction in Complex Models with Applications to Neutronics
Second Order Discretization of Bismut-Elworthy-Li Formula: Application to Sensitivity Analysis
Unbiased Inference for Discretely Observed Hidden Markov Model Diffusions
Importance Sampling and Necessary Sample Size: An Information Theory Approach
Known Boundary Emulation of Complex Computer Models
On Negative Transfer and Structure of Latent Functions in Multioutput Gaussian Processes
Finite Sample Approximations of Exact and Entropic Wasserstein Distances Between Covariance Operators and Gaussian Processes
An Efficient Numerical Method for Acoustic Wave Scattering in Random Media
Quantification of Errors Generated by Uncertain Data in a Linear Boundary Value Problem Using Neural Networks
Microstructure Models and Material Response by Extreme Value Theory
Bayesian Adaptive Reconstruction of Profile Optima and Optimizers
Comparison of Frequentist and Bayesian Confidence Analysis Methods on a Viscoelastic Stenosis Model
A Posteriori Analysis for Iterative Solvers for Nonautonomous Evolution Problems
A First Order Scheme for Backward Doubly Stochastic Differential Equations
Generating Nested Quadrature Rules with Positive Weights based on Arbitrary Sample Sets
Convergence Rates for the Maximum A Posteriori Estimator in PDE-Regression Models with Random Design
Choosing Observation Operators to Mitigate Model Error in Bayesian Inverse Problems
Bayesian Inference with Projected Densities
Degenerate Kalman Filter Error Covariances and Their Convergence onto the Unstable Subspace
Cross-Entropy-Based Importance Sampling with Failure-Informed Dimension Reduction for Rare Event Simulation
Quantifying the Effect of Random Dispersion for Logarithmic Schrödinger Equation
Dealing with Measurement Uncertainties as Nuisance Parameters in Bayesian Model Calibration
Bayesian Model and Dimension Reduction for Uncertainty Propagation: Applications in Random Media
Constraints versus Priors
Response Statistics for Random Heterogeneous Microstructures
Weighted Reduced Basis Method for Stochastic Optimal Control Problems with Elliptic PDE Constraint
Experimental Design for Nonparametric Correction of Misspecified Dynamical Models
A Low-Rank Solver for the Navier-Stokes Equations with Uncertain Viscosity
Stochastic Collocation Methods for Nonlinear Parabolic Equations with Random Coefficients
Wavenumber-Explicit Parametric Holomorphy of Helmholtz Solutions in the Context of Uncertainty Quantification
Transport Map Accelerated Markov Chain Monte Carlo
Sensitivity Analysis of Burgers' Equation with Shocks
Test Comparison for Sobol Indices over Nested Sets of Variables
Model Error Covariance Estimation for Weak Constraint Variational Data Assimilation
Covariance Expressions for Multifidelity Sampling with Multioutput, Multistatistic Estimators: Application to Approximate Control Variates
Optimal Learning in Experimental Design Using the Knowledge Gradient Policy with Application to Characterizing Nanoemulsion Stability