yusuke1997/dblp-id-title huggingface dataset:
Gibbs Priors for Bayesian Nonparametric Variable Selection with Weak Learners
Kriging Riemannian Data via Random Domain Decompositions
Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian Optimization
Robust Transformations for Multiple Regression via Additivity and Variance Stabilization
Nonlinear Functional Modeling Using Neural Networks
Logistic Regression Models for Aggregated Data
A Nearest Neighbor Open-Set Classifier based on Excesses of Distance Ratios
U -Statistical Inference for Hierarchical Clustering
Big Data Model Building Using Dimension Reduction and Sample Selection
Fast Variational Inference for Bayesian Factor Analysis in Single and Multi-Study Settings
Monte Carlo Simulation on the Stiefel Manifold via Polar Expansion
Biconvex Clustering
Computationally Efficient Learning of Gaussian Linear Structural Equation Models with Equal Error Variances
A Generalized Quantile Tree Method for Subgroup Identification
Sequential Learning of Regression Models by Penalized Estimation
Likelihood Evaluation of Jump-Diffusion Models Using Deterministic Nonlinear Filters
Modeling Intransitivity in Pairwise Comparisons with Application to Baseball Data
A Relabeling Approach to Handling the Class Imbalance Problem for Logistic Regression
Fast, Scalable Approximations to Posterior Distributions in Extended Latent Gaussian Models
Graded Matching for Large Observational Studies
Dynamic Prediction Using Landmark Historical Functional Cox Regression
Maximum Likelihood Estimation of Hierarchical Linear Models from Incomplete Data: Random Coefficients, Statistical Interactions, and Measurement Error
Backward Importance Sampling for Online Estimation of State Space Models
Comparing Two Samples Through Stochastic Dominance: A Graphical Approach
EM Algorithm for the Estimation of the RETAS Model
Adaptive Preferential Sampling in Phylodynamics With an Application to SARS-CoV-2
Improved Estimation of High-dimensional Additive Models Using Subspace Learning
Detecting Anomalous Time Series by GAMLSS-Akaike-Weights-Scoring
Statistical Inference in Circular Structural Model and Fitting Circles to Noisy Data
Change Point Detection in Dynamic Networks via Regularized Tensor Decomposition
Illumination Depth
The Block-Poisson Estimator for Optimally Tuned Exact Subsampling MCMC
Assessment and Adjustment of Approximate Inference Algorithms Using the Law of Total Variance
Relative Entropy Gradient Sampler for Unnormalized Distribution
Optimization for Calibration of Survey Weights under a Large Number of Conflicting Constraints
Asynchronous and Distributed Data Augmentation for Massive Data Settings
The Chi-Square Test of Distance Correlation
Leave-One-Out Kernel Density Estimates for Outlier Detection
Data Integration with Oracle Use of External Information from Heterogeneous Populations
Varying Coefficient Model via Adaptive Spline Fitting
Fast Calculation of Gaussian Process Multiple-Fold Cross-Validation Residuals and their Covariances
On the Wasserstein Median of Probability Measures
d-blink: Distributed End-to-End Bayesian Entity Resolution
Double-Matched Matrix Decomposition for Multi-View Data
A Framework for Leveraging Machine Learning Tools to Estimate Personalized Survival Curves
Functional Mixed Membership Models
MCVIS: A New Framework for Collinearity Discovery, Diagnostic, and Visualization
Model-Based Smoothing with Integrated Wiener Processes and Overlapping Splines
Survival Regression with Accelerated Failure Time Model in XGBoost
Model-Based Edge Clustering
Efficient Convex PCA with Applications to Wasserstein GPCA and Ranked Data
Functional Additive Models on Manifolds of Planar Shapes and Forms
Link Prediction for Egocentrically Sampled Networks
Generalized Tensor Decomposition With Features on Multiple Modes
Features of the Polynomial Biplot for Ordered Contingency Tables
Statement of Retraction: Detecting Anomalous Time Series by GAMLSS-Akaike-Weights-Scoring
A Plot is Worth a Thousand Tests: Assessing Residual Diagnostics with the Lineup Protocol
Versatile Descent Algorithms for Group Regularization and Variable Selection in Generalized Linear Models
MFAI: A Scalable Bayesian Matrix Factorization Approach to Leveraging Auxiliary Information
A Unified Algorithm for Penalized Convolution Smoothed Quantile Regression
Test and Visualization of Covariance Properties for Multivariate Spatio-Temporal Random Fields
Modeling Longitudinal Data Using Matrix Completion
Envelope Model for Function-on-Function Linear Regression
Analysis of Professional Basketball Field Goal Attempts via a Bayesian Matrix Clustering Approach
Probabilistic K -means with Local Alignment for Clustering and Motif Discovery in Functional Data
Marginally Calibrated Deep Distributional Regression
K -CDFs: A Nonparametric Clustering Algorithm via Cumulative Distribution Function
Online Updating of Survival Analysis
Quantum Annealing via Path-Integral Monte Carlo With Data Augmentation
Shrinking the Covariance Matrix Using Convex Penalties on the Matrix-Log Transformation
A Quantum Parallel Markov Chain Monte Carlo
Vecchia-Approximated Deep Gaussian Processes for Computer Experiments
General Nonlinear Function-on-Function Regression via Functional Universal Approximation
Recurrent Event Analysis in the Presence of Real-Time High Frequency Data via Random Subsampling
Distance-based Clustering of Functional Data with Derivative Principal Component Analysis
Times Square Sampling: An Adaptive Algorithm for Free Energy Estimation
An Exact Game-Theoretic Variable Importance Index for Generalized Additive Models
Matrix Autoregressive Spatio-Temporal Models
Simultaneous Coefficient Clustering and Sparsity for Multivariate Mixed Models
Quasi-Random Sampling for Multivariate Distributions via Generative Neural Networks
Generalized Variable Selection Algorithms for Gaussian Process Models by LASSO-Like Penalty
Quasi-Monte Carlo Methods for Binary Event Models with Complex Family Data
New and Simplified Manual Controls for Projection and Slice Tours, With Application to Exploring Classification Boundaries in High Dimensions
Independence-Encouraging Subsampling for Nonparametric Additive Models
Fully Three-Dimensional Radial Visualization
Clustering and Prediction With Variable Dimension Covariates
Accelerated and Interpretable Oblique Random Survival Forests
A User-Friendly Computational Framework for Robust Structured Regression with the L 2 Criterion
Ms.FPOP: A Fast Exact Segmentation Algorithm with a Multiscale Penalty
Improving and Extending STERGM Approximations Based on Cross-Sectional Data and Tie Durations
A Bayesian Singular Value Decomposition Procedure for Missing Data Imputation
Concave-Convex PDMP-based Sampling
Efficient Multidimensional Functional Data Analysis Using Marginal Product Basis Systems
A Reproducing Kernel Hilbert Space Framework for Functional Classification
Bootstrap Confidence Regions for Learned Feature Embeddings
Scalable Algorithms for Large Competing Risks Data
Markov Chain Importance Sampling - A Highly Efficient Estimator for MCMC
Mixtures of Matrix-Variate Contaminated Normal Distributions
Connecting the Dots: Numerical Randomized Hamiltonian Monte Carlo with State-Dependent Event Rates
Linear Manifold Modeling and Graph Estimation based on Multivariate Functional Data with Different Coarseness Scales
Spatial Linear Regression with Covariate Measurement Errors: Inference and Scalable Computation in a Functional Modeling Approach
Functional Nonlinear Learning
Spatial Shrinkage Via the Product Independent Gaussian Process Prior
Vertex Nomination Between Graphs via Spectral Embedding and Quadratic Programming
Model Selection With Lasso-Zero: Adding Straw to the Haystack to Better Find Needles
The Apogee to Apogee Path Sampler
A Relaxation Approach to Feature Selection for Linear Mixed Effects Models
A General Method for Deriving Tight Symbolic Bounds on Causal Effects
Nonparametric Subset Scanning for Detection of Heteroscedasticity
Learning Block Structured Graphs in Gaussian Graphical Models
Iterative Likelihood: A Unified Inference Tool
A Distributed Block-Split Gibbs Sampler with Hypergraph Structure for High-Dimensional Inverse Problems
Bayesian Model Choice for Directional Data
Pseudo-Marginal Inference for CTMCs on Infinite Spaces via Monotonic Likelihood Approximations
Integrated Depths for Partially Observed Functional Data
Fast Markov Chain Monte Carlo for High-Dimensional Bayesian Regression Models With Shrinkage Priors
Variance-Reduced Stochastic Optimization for Efficient Inference of Hidden Markov Models
Standardized Partial Sums and Products of p -Values
A Deep Dynamic Latent Block Model for Co-clustering of Zero-Inflated Data Matrices
Partition-Based Nonstationary Covariance Estimation Using the Stochastic Score Approximation
A Simple Divide-and-Conquer-based Distributed Method for the Accelerated Failure Time Model
Simultaneous Semiparametric Estimation of Clustering and Regression
Supervised Principal Component Regression for Functional Responses with High Dimensional Predictors
Ultra-Efficient MCMC for Bayesian Longitudinal Functional Data Analysis
MCMC Computations for Bayesian Mixture Models Using Repulsive Point Processes
Semiparametric Probit Regression Model with General Interval-Censored Failure Time Data
Estimation and Selection for High-Order Markov Chains with Bayesian Mixture Transition Distribution Models
Randomized Spectral Clustering in Large-Scale Stochastic Block Models
Clustering Sequence Data with Mixture Markov Chains with Covariates Using Multiple Simplex Constrained Optimization Routine (MSiCOR)
Cost-based Feature Selection for Network Model Choice
An Efficient Algorithm for Minimizing Multi Non-Smooth Component Functions
Interval-Censored Linear Quantile Regression
A Stochastic Approximation-Langevinized Ensemble Kalman Filter Algorithm for State Space Models with Unknown Parameters
Bayesian Semiparametric Covariate Informed Multivariate Density Deconvolution
Forecasting High-Dimensional Functional Time Series: Application to Sub-National Age-Specific Mortality
Structured Shrinkage Priors
The Holdout Randomization Test for Feature Selection in Black Box Models
Triangular Concordance Learning of Networks
Adaptive Design and Analysis Via Partitioning Trees for Emulation of a Complex Computer Code
Dimension Reduction Forests: Local Variable Importance Using Structured Random Forests
Influential Observations in Bayesian Regression Tree Models
Generalized Spatially Varying Coefficient Models
On the Use of Minimum Penalties in Statistical Learning
Generating Independent Replicates Directly from the Posterior Distribution for a Class of Spatial Hierarchical Models
Regularized Linear Programming Discriminant Rule with Folded Concave Penalty for Ultrahigh-Dimensional Data
Fast Bayesian Inference for Spatial Mean-Parameterized Conway-Maxwell-Poisson Models
Using Rejection Sampling Probability of Acceptance as a Measure of Independence
Model-Free Variable Selection With Matrix-Valued Predictors
Bayesian Heterogeneous Hidden Markov Models with an Unknown Number of States
Fast and Robust Low-Rank Learning over Networks: A Decentralized Matrix Quantile Regression Approach
A Distance-Preserving Matrix Sketch
Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory
Exhaustive Goodness of Fit Via Smoothed Inference and Graphics
Multivariate Contaminated Normal Censored Regression Model: Properties and Maximum Likelihood Inference
Robust Approximate Bayesian Inference With Synthetic Likelihood
Multivariate Singular Spectrum Analysis by Robust Diagonalwise Low-Rank Approximation
Variable Selection and Estimation for Misclassified Binary Responses and Multivariate Error-Prone Predictors
Dynamic Survival Prediction Using Sparse Longitudinal Images via Multi-Dimensional Functional Principal Component Analysis
The Integrated Nested Laplace Approximation for Fitting Dirichlet Regression Models
Confidence Bands for a Log-Concave Density
Local Inhomogeneous Weighted Summary Statistics for Marked Point Processes
A Generalization Gap Estimation for Overparameterized Models via the Langevin Functional Variance
Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models
Kernel Angle Dependence Measures in Metric Spaces
Distributed Censored Quantile Regression
Alternating Pruned Dynamic Programming for Multiple Epidemic Change-Point Estimation
Nonstationary Gaussian Process Discriminant Analysis With Variable Selection for High-Dimensional Functional Data
An Explicit Mean-Covariance Parameterization for Multivariate Response Linear Regression
Nonparametric and Semiparametric Quantile Regression via a New MM Algorithm
Statistical Analysis of Locally Parameterized Shapes
Fast Community Detection in Dynamic and Heterogeneous Networks
Local biplots for multidimensional scaling, with application to the microbiome
Hybrid Kronecker Product Decomposition and Approximation
Distributed Learning for Principal Eigenspaces without Moment Constraints
Micro-Macro Changepoint Inference for Periodic Data Sequences
Principal Variables Analysis for Non-Gaussian Data
Covariance Assisted Multivariate Penalized Additive Regression (CoMPAdRe)
Wasserstein-Kaplan-Meier Survival Regression
Manifold Optimization-Assisted Gaussian Variational Approximation
A Penalized Criterion for Selecting the Number of Clusters for K-Medians
Predictive Subdata Selection for Computer Models
Multivariate Functional Regression Via Nested Reduced-Rank Regularization
Joint and Individual Component Regression
Statistical Significance of Clustering with Multidimensional Scaling
GEE-Assisted Forward Regression for Spatial Latent Variable Models
Model Interpretation Through Lower-Dimensional Posterior Summarization
Fused-Lasso Regularized Cholesky Factors of Large Nonstationary Covariance Matrices of Replicated Time Series
Generalized Linear Mixed Models With Crossed Effects and Unit-specific Survey Weights
On Construction and Estimation of Stationary Mixture Transition Distribution Models
Dermoscopic Image Classification with Neural Style Transfer
Change-Point Detection for Graphical Models in the Presence of Missing Values
Iteratively Reweighted Least Squares Method for Estimating Polyserial and Polychoric Correlation Coefficients
High-Dimensional Multivariate Linear Regression with Weighted Nuclear Norm Regularization
Convergence of Position-Dependent MALA with Application to Conditional Simulation in GLMMs
Copula Graphical Models for Heterogeneous Mixed Data
Nonlinear Variable Selection via Deep Neural Networks
Adaptive Bayesian SLOPE: Model Selection With Incomplete Data
Generative Quantile Regression with Variability Penalty
Joint Network Reconstruction and Community Detection from Rich but Noisy Data
The G -Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models
Accelerated Componentwise Gradient Boosting Using Efficient Data Representation and Momentum-Based Optimization
Flexible and Fast Spatial Return Level Estimation Via a Spatially Fused Penalty
Model Checking for Logistic Models When the Number of Parameters Tends to Infinity
Search Algorithms and Loss Functions for Bayesian Clustering
Spectral Clustering via Adaptive Layer Aggregation for Multi-Layer Networks
Quantizing Rare Random Maps: Application to Flooding Visualization
Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph Clustering
Universal Inference Meets Random Projections: A Scalable Test for Log-Concavity
Bayesian Tree-Structured Two-Level Clustering for Nested Data Analysis
Model-Based Microbiome Data Ordination: A Variational Approximation Approach
Latent Space Model for Higher-Order Networks and Generalized Tensor Decomposition
Hierarchical Total Variations and Doubly Penalized ANOVA Modeling for Multivariate Nonparametric Regression
Accelerated Structured Matrix Factorization
Performance Is Not Enough: The Story Told by a Rashomon Quartet
Adaptive Bayesian Spectral Analysis of High-Dimensional Nonstationary Time Series
Functional Regression for Densely Observed Data With Novel Regularization
Rapid Bayesian Inference for Expensive Stochastic Models
Two-Dimensional Functional Principal Component Analysis for Image Feature Extraction
Branching Process Models to Identify Risk Factors for Infectious Disease Transmission
Computational Methods for Fast Bayesian Model Assessment via Calibrated Posterior p -values
A Probit Tensor Factorization Model For Relational Learning
A Multi-Attribute Evaluation of Genotype-Environment Experiments Using Biplots and Joint Plots Graphics
Sparse Functional Boxplots for Multivariate Curves
On Inference for Modularity Statistics in Structured Networks
Fast, Approximate Maximum Likelihood Estimation of Log-Gaussian Cox Processes
Optimal Sampling for Generalized Linear Models Under Measurement Constraints
Eye Fitting Straight Lines in the Modern Era
Stochastic Block Smooth Graphon Model
clusterMLD: An Efficient Hierarchical Clustering Method for Multivariate Longitudinal Data
Adaptive Handling of Dependence in High-Dimensional Regression Modeling
Blocked Gibbs Sampler for Hierarchical Dirichlet Processes
Flexible Variational Bayes Based on a Copula of a Mixture
Importance Sampling with the Integrated Nested Laplace Approximation
Bayesian Model Selection in Additive Partial Linear Models Via Locally Adaptive Splines
The Mellin Transform to Manage Quadratic Forms in Normal Random Variables
Smoothing Splines for Discontinuous Signals
Variational Inference based on a Subclass of Closed Skew Normals
Testing Biased Randomization Assumptions and Quantifying Imperfect Matching and Residual Confounding in Matched Observational Studies
Efficient Sampling and Structure Learning of Bayesian Networks
Multi-Resolution Filters for Massive Spatio-Temporal Data
Fast Matrix-Free Methods for Model-Based Personalized Synthetic MR Imaging
Projected Pólya Tree
Mixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingness
Efficient Posterior Sampling for Bayesian Poisson Regression
FAStEN: An Efficient Adaptive Method for Feature Selection and Estimation in High-Dimensional Functional Regressions
Discovering Active Subspaces for High-Dimensional Computer Models
Structural Discovery with Partial Ordering Information for Time-Dependent Data with Convergence Guarantees
False Discovery Rates to Detect Signals from Incomplete Spatially Aggregated Data
Penguins Go Parallel: A Grammar of Graphics Framework for Generalized Parallel Coordinate Plots
Smooth Multi-Period Forecasting With Application to Prediction of COVID-19 Cases
On Moments of Folded and Doubly Truncated Multivariate Extended Skew-Normal Distributions
Locally Sparse Function-on-Function Regression
Algorithms for Sparse Support Vector Machines
Random Forest Adjustment for Approximate Bayesian Computation
A General Framework for Identifying Hierarchical Interactions and Its Application to Genomics Data
Distributed Bayesian Inference in Linear Mixed-Effects Models
Spatially Smoothed Robust Covariance Estimation for Local Outlier Detection
Wavelet Feature Screening
Generalized Connectivity Matrix Response Regression with Applications in Brain Connectivity Studies
Vecchia Likelihood Approximation for Accurate and Fast Inference with Intractable Spatial Max-Stable Models
Log-Rank-Type Tests for Equality of Distributions in High-Dimensional Spaces
Exact Bayesian Inference for Level-Set Cox Processes with Piecewise Constant Intensity Function
Exact Bayesian Inference for Discretely Observed Markov Jump Processes Using Finite Rate Matrices
Nonstationary Modeling With Sparsity for Spatial Data via the Basis Graphical Lasso
Meta Clustering for Collaborative Learning
High-Dimensional Multi-Task Learning using Multivariate Regression and Generalized Fiducial Inference
Competing Risk Modeling with Bivariate Varying Coefficients to Understand the Dynamic Impact of COVID-19
Assessing and Visualizing Simultaneous Simulation Error
A Projection Pursuit Forest Algorithm for Supervised Classification
An Exact Auxiliary Variable Gibbs Sampler for a Class of Diffusions
Dependent Modeling of Temporal Sequences of Random Partitions
Communication-Efficient Nonparametric Quantile Regression via Random Features
Bayesian Computation in Dynamic Latent Factor Models
On Simulating Skewed and Cluster-Weighted Data for Studying Performance of Clustering Algorithms
Smooth and Probabilistic PARAFAC Model with Auxiliary Covariates
Mixture of Linear Models Co-supervised by Deep Neural Networks
Trace Ratio Optimization for High-Dimensional Multi-Class Discrimination
Learning Multiple Quantiles With Neural Networks
Maximum Likelihood Algorithm for Spatial Generalized Linear Mixed Models without Numerical Evaluations of Intractable Integrals
Block-Wise Variable Selection for Clustering Via Latent States of Mixture Models
Efficient Bayesian Synthetic Likelihood With Whitening Transformations
Sensitivity Analysis of Pandemic Models Can Support Effective Policy Decisions
LowCon: A Design-based Subsampling Approach in a Misspecified Linear Model
Efficient Approximation of Gromov-Wasserstein Distance Using Importance Sparsification
Global Likelihood Sampler for Multimodal Distributions
Deeply Learned Generalized Linear Models with Missing Data
Bayesian Multi-Task Variable Selection with an Application to Differential DAG Analysis
Deep Learning With Functional Inputs
Group Selection and Shrinkage: Structured Sparsity for Semiparametric Additive Models
Properties of Test Statistics for Nonparametric Cointegrating Regression Functions Based on Subsamples
Sample-Wise Combined Missing Effect Model with Penalization
AdaptSPEC-X: Covariate-Dependent Spectral Modeling of Multiple Nonstationary Time Series
Nonparametric Testing of the Covariate Significance for Spatial Point Patterns under the Presence of Nuisance Covariates
Optimal Decorrelated Score Subsampling for High-Dimensional Generalized Linear Models Under Measurement Constraints
Bayesian Change Point Detection with Spike-and-Slab Priors
Efficient Nonparametric Estimation of 3D Point Cloud Signals through Distributed Learning
Estimation and Model Selection for Nonparametric Function-on-Function Regression
Bayesian Variable Selection for Gaussian Copula Regression Models
Linear Time Dynamic Programming for Computing Breakpoints in the Regularization Path of Models Selected From a Finite Set
Assessment of Regression Models With Discrete Outcomes Using Quasi-Empirical Residual Distribution Functions
Graphical and Computational Tools to Guide Parameter Choice for the Cluster Weighted Robust Model
Adaptive Semiparametric Bayesian Differential Equations Via Sequential Monte Carlo
DeepMoM: Robust Deep Learning With Median-of-Means
Functional Linear Model with Prior Information of Subjects' Network
Interpretable Architecture Neural Networks for Function Visualization
Efficient Learning of Quadratic Variance Function Directed Acyclic Graphs via Topological Layers
A Simple Algorithm for Exact Multinomial Tests
Generative Multi-Purpose Sampler for Weighted M-estimation
Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions
Enforcing Stationarity through the Prior in Vector Autoregressions
Supervised Stratified Subsampling for Predictive Analytics
Cluster Optimized Proximity Scaling
Forward Stepwise Deep Autoencoder-Based Monotone Nonlinear Dimensionality Reduction Methods
Bayesian Kernel Two-Sample Testing
On Exact Feature Screening in Ultrahigh-Dimensional Binary Classification
Online Smoothing for Diffusion Processes Observed with Noise
Learning Subspaces of Different Dimensions
Variational Bayesian Neural Networks via Resolution of Singularities
Transfer Learning of Individualized Treatment Rules from Experimental to Real-World Data
A Scalable Method to Exploit Screening in Gaussian Process Models with Noise
Bootstrapped Edge Count Tests for Nonparametric Two-Sample Inference Under Heterogeneity
A Dirichlet Model of Alignment Cost in Mixed-Membership Unsupervised Clustering
Distributed Decoding From Heterogeneous 1-Bit Compressive Measurements
Scalable Feature Matching Across Large Data Collections
Improving the Accuracy of Marginal Approximations in Likelihood-Free Inference via Localization
Estimation of the Spatial Weighting Matrix for Spatiotemporal Data under the Presence of Structural Breaks
Design Principles for Data Analysis