{"total":30,"rows":[{"id":"2102.12108","title":"The Promises and Pitfalls of Deep Kernel Learning","created":"2021-02-24","cited_by_count":81,"area":null},{"id":"2206.03992","title":"Neural Diffusion Processes","created":"2022-06-08","cited_by_count":50,"area":null},{"id":"2205.10186","title":"Bayesian Active Learning with Fully Bayesian Gaussian Processes","created":"2022-05-20","cited_by_count":26,"area":null},{"id":"2304.11005","title":"Self-Correcting Bayesian Optimization through Bayesian Active Learning","created":"2023-04-21","cited_by_count":22,"area":null},{"id":"2303.04756","title":"Meta-learning Control Variates: Variance Reduction with Limited Data","created":"2023-03-08","cited_by_count":7,"area":null},{"id":"2306.05843","title":"Adaptive Batch Sizes for Active Learning A Probabilistic Numerics Approach","created":"2023-06-09","cited_by_count":6,"area":null},{"id":"2105.00894","title":"How Bayesian Should Bayesian Optimisation Be?","created":"2021-05-03","cited_by_count":5,"area":null},{"id":"2202.07595","title":"Bayesian Optimisation for Active Monitoring of Air Pollution","created":"2022-02-15","cited_by_count":5,"area":null},{"id":"2208.06028","title":"Gaussian Process Surrogate Models for Neural Networks","created":"2022-08-11","cited_by_count":5,"area":null},{"id":"2209.15609","title":"$\\Phi$-DVAE: Physics-Informed Dynamical Variational Autoencoders for Unstructured Data Assimilation","created":"2022-09-30","cited_by_count":5,"area":null},{"id":"2306.14731","title":"Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regression","created":"2023-06-26","cited_by_count":5,"area":null},{"id":"2309.11875","title":"Stochastic stiffness identification and response estimation of Timoshenko beams via physics-informed Gaussian processes","created":"2023-09-21","cited_by_count":5,"area":null},{"id":"2211.02218","title":"Fully Bayesian inference for latent variable Gaussian process models","created":"2022-11-04","cited_by_count":4,"area":null},{"id":"2211.02476","title":"Sparse Gaussian Process Hyperparameters: Optimize or Integrate?","created":"2022-11-04","cited_by_count":4,"area":null},{"id":"2308.02894","title":"Physics-informed Gaussian process model for Euler-Bernoulli beam elements","created":"2023-08-05","cited_by_count":4,"area":null},{"id":"2408.06069","title":"Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations","created":"2024-08-12","cited_by_count":4,"area":null},{"id":"2503.05574","title":"BARK: A Fully Bayesian Tree Kernel for Black-box Optimization","created":"2025-03-07","cited_by_count":3,"area":null},{"id":"2510.23681","title":"Informed Initialization for Bayesian Optimization and Active Learning","created":"2025-10-27","cited_by_count":2,"area":null},{"id":"2304.11654","title":"Stochastic Cell Transmission Models of Traffic Networks","created":"2023-04-23","cited_by_count":1,"area":null},{"id":"2311.14653","title":"Data-driven Prior Learning for Bayesian Optimisation","created":"2023-11-24","cited_by_count":1,"area":null},{"id":"2110.15049","title":"Validating Gaussian Process Models with Simulation-Based Calibration","created":"2021-10-27","cited_by_count":0,"area":null},{"id":"2203.08061","title":"A novel sampler for Gauss-Hermite determinantal point processes with application to Monte Carlo integration","created":"2022-03-15","cited_by_count":0,"area":null},{"id":"2204.12927","title":"A Bayesian Approach To Graph Partitioning","created":"2022-04-24","cited_by_count":0,"area":null},{"id":"2208.13960","title":"The case for fully Bayesian optimisation in small-sample trials","created":"2022-08-30","cited_by_count":0,"area":null},{"id":"2210.10973","title":"Scalable Bayesian Transformed Gaussian Processes","created":"2022-10-20","cited_by_count":0,"area":null},{"id":"2303.14291","title":"Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holes","created":"2023-03-24","cited_by_count":0,"area":null},{"id":"2402.17704","title":"Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic Assays","created":"2024-02-27","cited_by_count":0,"area":null},{"id":"2406.04796","title":"Robust Inference of Dynamic Covariance Using Wishart Processes and Sequential Monte Carlo","created":"2024-06-07","cited_by_count":0,"area":null},{"id":"2409.12919","title":"Swine Diet Design using Multi-objective Regionalized Bayesian Optimization","created":"2024-09-19","cited_by_count":0,"area":null},{"id":"2511.16815","title":"BITS for GAPS: Bayesian Information-Theoretic Sampling for hierarchical GAussian Process Surrogates","created":"2025-11-20","cited_by_count":0,"area":null}],"next_cursor":null}