{"total":43,"rows":[{"id":"2206.02218","title":"Statistical Deep Learning for Spatial and Spatio-Temporal Data","created":"2022-06-05","cited_by_count":15,"area":null},{"id":"2304.09157","title":"Neural networks for geospatial data","created":"2023-04-18","cited_by_count":11,"area":null},{"id":"2305.04634","title":"Neural Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihoods","created":"2023-05-08","cited_by_count":10,"area":null},{"id":"2205.09070","title":"Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels","created":"2022-05-18","cited_by_count":9,"area":null},{"id":"2104.14581","title":"MuyGPs: Scalable Gaussian Process Hyperparameter Estimation Using Local Cross-Validation","created":"2021-04-29","cited_by_count":8,"area":null},{"id":"2202.12981","title":"Scalable Gaussian-process regression and variable selection using Vecchia approximations","created":"2022-02-25","cited_by_count":8,"area":null},{"id":"2310.12000","title":"Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models","created":"2023-10-18","cited_by_count":6,"area":null},{"id":"2412.01098","title":"Spatial Conformal Inference through Localized Quantile Regression","created":"2024-12-02","cited_by_count":6,"area":null},{"id":"2101.11751","title":"Faster Kernel Interpolation for Gaussian Processes","created":"2021-01-28","cited_by_count":5,"area":null},{"id":"2210.07893","title":"Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees","created":"2022-10-14","cited_by_count":5,"area":null},{"id":"2301.13303","title":"Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization","created":"2023-01-30","cited_by_count":4,"area":null},{"id":"2405.14492","title":"Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Data","created":"2024-05-23","cited_by_count":4,"area":null},{"id":"2109.00046","title":"Scalable Spatiotemporally Varying Coefficient Modelling with Bayesian Kernelized Tensor Regression","created":"2021-08-31","cited_by_count":3,"area":null},{"id":"2512.09060","title":"All Emulators are Wrong, Many are Useful, and Some are More Useful Than Others: A Reproducible Comparison of Computer Model Surrogates","created":"2025-12-09","cited_by_count":3,"area":null},{"id":"2109.04447","title":"Modeling Massive Spatial Datasets Using a Conjugate Bayesian Linear Regression Framework","created":"2021-09-09","cited_by_count":2,"area":null},{"id":"2208.09978","title":"Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Data","created":"2022-08-21","cited_by_count":2,"area":null},{"id":"2411.05869","title":"Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data","created":"2024-11-07","cited_by_count":2,"area":null},{"id":"2502.08730","title":"New Bounds for Sparse Variational Gaussian Processes","created":"2025-02-12","cited_by_count":2,"area":null},{"id":"2505.24066","title":"Adaptive Resolution for Finite-Rank Gaussian Processes","created":"2025-05-29","cited_by_count":2,"area":null},{"id":"2506.00270","title":"Bayesian Data Sketching for Varying Coefficient Regression Models","created":"2025-05-30","cited_by_count":2,"area":null},{"id":"2512.02925","title":"Fast Gaussian Process Approximations for Autocorrelated Data","created":"2025-12-02","cited_by_count":2,"area":null},{"id":"2103.03321","title":"On MCMC for variationally sparse Gaussian processes: A pseudo-marginal approach","created":"2021-03-04","cited_by_count":1,"area":null},{"id":"2205.10879","title":"Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation","created":"2022-05-22","cited_by_count":1,"area":null},{"id":"2208.06528","title":"Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models","created":"2022-08-12","cited_by_count":1,"area":null},{"id":"2208.14592","title":"Light curve completion and forecasting using fast and scalable Gaussian processes (MuyGPs)","created":"2022-08-31","cited_by_count":1,"area":null},{"id":"2305.13879","title":"Stochastic PDE representation of random fields for large-scale Gaussian process regression and statistical finite element analysis","created":"2023-05-23","cited_by_count":1,"area":null},{"id":"2407.02740","title":"Implementation and Analysis of GPU Algorithms for Vecchia Approximation","created":"2024-07-03","cited_by_count":1,"area":null},{"id":"2407.17697","title":"Superior Scoring Rules for Probabilistic Evaluation of Single-Label Multi-Class Classification Tasks","created":"2024-07-25","cited_by_count":1,"area":null},{"id":"2409.16308","title":"Probabilistic Spatiotemporal Modeling of Day-Ahead Wind Power Generation with Input-Warped Gaussian Processes","created":"2024-09-10","cited_by_count":1,"area":null},{"id":"2410.08311","title":"Correspondence of NNGP Kernel and the Matern Kernel","created":"2024-10-10","cited_by_count":1,"area":null},{"id":"2502.06067","title":"Smooth Sailing: Lipschitz-Driven Uncertainty Quantification for Spatial Association","created":"2025-02-09","cited_by_count":1,"area":null},{"id":"2505.09803","title":"LatticeVision: Image to Image Networks for Modeling Non-Stationary Spatial Data","created":"2025-05-14","cited_by_count":1,"area":null},{"id":"2607.00051","title":"Spatio-Temporal Gaussian Process for Building Terrain-Incorporating Wind Power Curves","created":"2026-06-30","cited_by_count":1,"area":null},{"id":"2209.11280","title":"Scalable Gaussian Process Hyperparameter Optimization via Coverage Regularization","created":"2022-09-22","cited_by_count":0,"area":null},{"id":"2311.03201","title":"Spatial Process Approximations: Assessing Their Necessity","created":"2023-11-06","cited_by_count":0,"area":null},{"id":"2408.07480","title":"Adaptive Basis Function Selection for Computationally Efficient Predictions","created":"2024-08-14","cited_by_count":0,"area":null},{"id":"2412.20323","title":"When the whole is greater than the sum of its parts: Scaling black-box inference to large data settings through divide-and-conquer","created":"2024-12-29","cited_by_count":0,"area":null},{"id":"2503.21473","title":"DeepRV: Accelerating Spatiotemporal Inference with Pre-trained Neural Priors","created":"2025-03-27","cited_by_count":0,"area":null},{"id":"2507.05064","title":"Vecchia-Inducing-Points Full-Scale Approximations for Gaussian Processes","created":"2025-07-07","cited_by_count":0,"area":null},{"id":"2508.01864","title":"Fast Gaussian process inference by exact Mat\\'ern kernel decomposition","created":"2025-08-03","cited_by_count":0,"area":null},{"id":"2509.12051","title":"A comparison between geostatistical and machine learning models for spatio-temporal prediction of PM2.5 data","created":"2025-09-15","cited_by_count":0,"area":null},{"id":"2511.00366","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","created":"2025-11-01","cited_by_count":0,"area":null},{"id":"2609.30348","title":"Adaptive multi-resolution Gaussian processes: Scalable exact inference with naturally data-sparse covariance matrices","created":"2026-09-24","cited_by_count":0,"area":null}],"next_cursor":null}