{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/gaussian-processes/papers/11","list_of":"/task/gaussian-processes","task":"Gaussian Processes","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":11,"pages_in_order":20,"rows_per_page":100,"rows":[1001,1100],"of":1963,"counts":{"archive_papers_tagged":1963,"with_a_code_link":685,"where_syntology_ran_a_sample":160,"not_listed_spam_title":0,"listed":1963,"listed_where_code_ran":160,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":131,"every_run_a_failure_of_syntologys_instrument":29,"listed_with_a_run_with_no_instrument_failure":131,"listed_every_run_a_failure_of_syntologys_instrument":29,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/gaussian-processes","prev":"/task/gaussian-processes/papers/10","next":"/task/gaussian-processes/papers/12","papers":[{"url":null,"slug":"stochastic-mpc-for-energy-hubs-using-data","title":"Stochastic MPC for energy hubs using data driven demand forecasting","date":"2023-04-24","arxiv_id":"2304.12438","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-correcting-bayesian-optimization-through","title":"Self-Correcting Bayesian Optimization through Bayesian Active Learning","date":"2023-04-21","arxiv_id":"2304.11005","repositories_listed":0,"syntology":null},{"url":null,"slug":"martingale-posterior-neural-processes","title":"Martingale Posterior Neural Processes","date":"2023-04-19","arxiv_id":"2304.09431","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-networks-for-geospatial-data","title":"Neural networks for geospatial data","date":"2023-04-18","arxiv_id":"2304.09157","repositories_listed":0,"syntology":null},{"url":null,"slug":"dimensionality-reduction-as-probabilistic","title":"Dimensionality Reduction as Probabilistic Inference","date":"2023-04-15","arxiv_id":"2304.07658","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-event-triggered-online-learning","title":"Cooperative Online Learning for Multi-Agent System Control via Gaussian Processes with Event-Triggered Mechanism: Extended Version","date":"2023-04-11","arxiv_id":"2304.05138","repositories_listed":0,"syntology":null},{"url":null,"slug":"wide-neural-networks-from-non-gaussian-random","title":"Wide neural networks: From non-gaussian random fields at initialization to the NTK geometry of training","date":"2023-04-06","arxiv_id":"2304.03385","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-neural-processes-for-uncertainty","title":"Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation","date":"2023-04-04","arxiv_id":"2304.01518","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-gaussian-processes-with-spherical-1","title":"Sparse Gaussian Processes with Spherical Harmonic Features Revisited","date":"2023-03-28","arxiv_id":"2303.15948","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-model-predictive-control-utilizing","title":"Stochastic Model Predictive Control Utilizing Bayesian Neural Networks","date":"2023-03-25","arxiv_id":"2303.14519","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-based-on-mixtures-of-sparse","title":"Clustering based on Mixtures of Sparse Gaussian Processes","date":"2023-03-23","arxiv_id":"2303.13665","repositories_listed":0,"syntology":null},{"url":null,"slug":"chance-constrained-stochastic-optimal-control-2","title":"Chance Constrained Stochastic Optimal Control for Arbitrarily Disturbed LTI Systems Via the One-Sided Vysochanskij-Petunin Inequality","date":"2023-03-22","arxiv_id":"2303.12295","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-process-on-the-product-of","title":"Gaussian Process on the Product of Directional Manifolds","date":"2023-03-13","arxiv_id":"2303.06799","repositories_listed":0,"syntology":null},{"url":null,"slug":"reconstructing-the-hubble-parameter-with","title":"Reconstructing the Hubble parameter with future Gravitational Wave missions using Machine Learning","date":"2023-03-09","arxiv_id":"2303.05169","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-predictive-control-with-gaussian-1","title":"Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles","date":"2023-03-08","arxiv_id":"2303.04725","repositories_listed":0,"syntology":null},{"url":null,"slug":"safe-machine-learning-supported-model","title":"Safe Machine-Learning-supported Model Predictive Force and Motion Control in Robotics","date":"2023-03-08","arxiv_id":"2303.04569","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-based-position-and-stiffness","title":"Learning-based Position and Stiffness Feedforward Control of Antagonistic Soft Pneumatic Actuators using Gaussian Processes","date":"2023-03-03","arxiv_id":"2303.01840","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-kernelized-tensor-factorization-as","title":"Bayesian Kernelized Tensor Factorization as Surrogate for Bayesian Optimization","date":"2023-02-28","arxiv_id":"2302.14510","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-sensor-placement-from-regression","title":"Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces","date":"2023-02-28","arxiv_id":"2303.00028","repositories_listed":0,"syntology":null},{"url":null,"slug":"random-forests-for-binary-geospatial-data","title":"Random forests for binary geospatial data","date":"2023-02-27","arxiv_id":"2302.13828","repositories_listed":0,"syntology":null},{"url":null,"slug":"sharp-calibrated-gaussian-processes","title":"Sharp Calibrated Gaussian Processes","date":"2023-02-23","arxiv_id":"2302.11961","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-separable-covariance-kernels-for","title":"Non-separable Covariance Kernels for Spatiotemporal Gaussian Processes based on a Hybrid Spectral Method and the Harmonic Oscillator","date":"2023-02-19","arxiv_id":"2302.09580","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-meta-learning-approach-to-population-based","title":"A Meta-Learning Approach to Population-Based Modelling of Structures","date":"2023-02-15","arxiv_id":"2302.07980","repositories_listed":0,"syntology":null},{"url":null,"slug":"fully-bayesian-autoencoders-with-latent","title":"Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes","date":"2023-02-09","arxiv_id":"2302.04534","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-shrinkage-gaussian-processes","title":"Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery","date":"2023-02-01","arxiv_id":"2302.00755","repositories_listed":0,"syntology":null},{"url":null,"slug":"short-term-prediction-and-filtering-of-solar","title":"Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes","date":"2023-02-01","arxiv_id":"2302.00388","repositories_listed":0,"syntology":null},{"url":null,"slug":"nonlinearities-in-macroeconomic-tail-risk","title":"Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions","date":"2023-01-31","arxiv_id":"2301.13604","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-fully-automated-framework-integrating","title":"A Fully-Automated Framework Integrating Gaussian Process Regression and Bayesian Optimization to Design Pin-Fins","date":"2023-01-30","arxiv_id":"2301.13118","repositories_listed":0,"syntology":null},{"url":null,"slug":"intrinsic-bayesian-optimisation-on-complex","title":"Intrinsic Bayesian Optimisation on Complex Constrained Domain","date":"2023-01-29","arxiv_id":"2301.12581","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequential-estimation-of-gaussian-process","title":"Sequential Estimation of Gaussian Process-based Deep State-Space Models","date":"2023-01-29","arxiv_id":"2301.12528","repositories_listed":0,"syntology":null},{"url":null,"slug":"inducing-point-allocation-for-sparse-gaussian","title":"Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation","date":"2023-01-24","arxiv_id":"2301.10123","repositories_listed":0,"syntology":null},{"url":null,"slug":"stock-trading-optimization-through-model-1","title":"Model Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization","date":"2023-01-23","arxiv_id":"2301.09297","repositories_listed":0,"syntology":null},{"url":null,"slug":"intrinsic-gaussian-process-on-unknown","title":"Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics","date":"2023-01-16","arxiv_id":"2301.06533","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-role-of-model-uncertainties-in","title":"On the role of Model Uncertainties in Bayesian Optimization","date":"2023-01-14","arxiv_id":"2301.05983","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-methods-for-prediction-of-1","title":"Machine learning methods for prediction of breakthrough curves in reactive porous media","date":"2023-01-12","arxiv_id":"2301.04998","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-the-evolution-of-temporal-knowledge","title":"Modeling the evolution of temporal knowledge graphs with uncertainty","date":"2023-01-12","arxiv_id":"2301.04977","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-machine-learning-to-gas","title":"Application of machine learning to gas flaring","date":"2023-01-11","arxiv_id":"2301.04141","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-bayesian-target-value-optimization","title":"Robust Bayesian Target Value Optimization","date":"2023-01-11","arxiv_id":"2301.04344","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantile-autoregression-based-non-causality","title":"Quantile Autoregression-based Non-causality Testing","date":"2023-01-07","arxiv_id":"2301.02937","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-data-driven-gaussian-process-filter-for","title":"A Data-Driven Gaussian Process Filter for Electrocardiogram Denoising","date":"2023-01-06","arxiv_id":"2301.02607","repositories_listed":0,"syntology":null},{"url":null,"slug":"extrinsic-bayesian-optimizations-on-manifolds","title":"Extrinsic Bayesian Optimizations on Manifolds","date":"2022-12-21","arxiv_id":"2212.13886","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-note-on-the-smallest-eigenvalue-of-the","title":"A note on the smallest eigenvalue of the empirical covariance of causal Gaussian processes","date":"2022-12-19","arxiv_id":"2212.09508","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-applied-to-computational","title":"Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics","date":"2022-12-18","arxiv_id":"2212.08989","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-structured-normalizing-flow","title":"Generative structured normalizing flow Gaussian processes applied to spectroscopic data","date":"2022-12-14","arxiv_id":"2212.07554","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-process-barrier-states-for-safe","title":"Gaussian Process Barrier States for Safe Trajectory Optimization and Control","date":"2022-12-01","arxiv_id":"2212.00268","repositories_listed":0,"syntology":null},{"url":null,"slug":"safe-model-free-reinforcement-learning-using","title":"Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functions","date":"2022-11-30","arxiv_id":"2211.17250","repositories_listed":0,"syntology":null},{"url":null,"slug":"frequency-domain-gaussian-process-models-for","title":"Frequency Domain Gaussian Process Models for $H^\\infty$ Uncertainties","date":"2022-11-29","arxiv_id":"2211.15923","repositories_listed":0,"syntology":null},{"url":null,"slug":"transductive-kernels-for-gaussian-processes","title":"Transductive Kernels for Gaussian Processes on Graphs","date":"2022-11-28","arxiv_id":"2211.15322","repositories_listed":0,"syntology":null},{"url":null,"slug":"rectified-pessimistic-optimistic-learning-for","title":"Rectified Pessimistic-Optimistic Learning for Stochastic Continuum-armed Bandit with Constraints","date":"2022-11-27","arxiv_id":"2211.14720","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-gaussian-process-regression-based-dynamical","title":"A Gaussian Process Regression based Dynamical Models Learning Algorithm for Target Tracking","date":"2022-11-25","arxiv_id":"2211.14162","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-deep-neural-networks-by-iterative","title":"Understanding Sparse Feature Updates in Deep Networks using Iterative Linearisation","date":"2022-11-22","arxiv_id":"2211.12345","repositories_listed":0,"syntology":null},{"url":null,"slug":"safe-optimization-of-an-industrial","title":"Safe Optimization of an Industrial Refrigeration Process Using an Adaptive and Explorative Framework","date":"2022-11-21","arxiv_id":"2211.13019","repositories_listed":0,"syntology":null},{"url":null,"slug":"approximate-uncertainty-propagation-for","title":"The Past Does Matter: Correlation of Subsequent States in Trajectory Predictions of Gaussian Process Models","date":"2022-11-20","arxiv_id":"2211.11103","repositories_listed":0,"syntology":null},{"url":null,"slug":"expert-selection-in-distributed-gaussian","title":"Entry Dependent Expert Selection in Distributed Gaussian Processes Using Multilabel Classification","date":"2022-11-17","arxiv_id":"2211.09940","repositories_listed":0,"syntology":null},{"url":null,"slug":"introduction-and-exemplars-of-uncertainty","title":"Introduction and Exemplars of Uncertainty Decomposition","date":"2022-11-17","arxiv_id":"2211.15475","repositories_listed":0,"syntology":null},{"url":null,"slug":"global-optimization-with-parametric-function","title":"Global Optimization with Parametric Function Approximation","date":"2022-11-16","arxiv_id":"2211.09100","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatiotemporal-modeling-of-european","title":"Spatiotemporal modeling of European paleoclimate using doubly sparse Gaussian processes","date":"2022-11-15","arxiv_id":"2211.08160","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-neural-optimal-interpolation-models","title":"Learning Neural Optimal Interpolation Models and Solvers","date":"2022-11-14","arxiv_id":"2211.07209","repositories_listed":0,"syntology":null},{"url":null,"slug":"pac-bayesian-meta-learning-from-theory-to","title":"Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice","date":"2022-11-14","arxiv_id":"2211.07206","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-improved-learning-in-gaussian","title":"Towards Improved Learning in Gaussian Processes: The Best of Two Worlds","date":"2022-11-11","arxiv_id":"2211.06260","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-power-sum-kernels-on-symmetric-groups","title":"On power sum kernels on symmetric groups","date":"2022-11-10","arxiv_id":"2211.05650","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-output-gaussian-processes-for-inverse","title":"Multi-output Gaussian processes for inverse uncertainty quantification in neutron noise analysis","date":"2022-11-04","arxiv_id":"2211.02465","repositories_listed":0,"syntology":null},{"url":null,"slug":"fantasizing-with-dual-gps-in-bayesian","title":"Fantasizing with Dual GPs in Bayesian Optimization and Active Learning","date":"2022-11-02","arxiv_id":"2211.01053","repositories_listed":0,"syntology":null},{"url":null,"slug":"monte-carlo-tree-descent-for-black-box","title":"Monte Carlo Tree Descent for Black-Box Optimization","date":"2022-11-01","arxiv_id":"2211.00778","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-output-regulation-via-gaussian","title":"Data-driven Output Regulation via Gaussian Processes and Luenberger Internal Models","date":"2022-10-28","arxiv_id":"2210.15938","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimization-on-manifolds-via-graph-gaussian","title":"Optimization on Manifolds via Graph Gaussian Processes","date":"2022-10-20","arxiv_id":"2210.10962","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-bayesian-transformed-gaussian","title":"Scalable Bayesian Transformed Gaussian Processes","date":"2022-10-20","arxiv_id":"2210.10973","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-disentanglement-with-non","title":"Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning","date":"2022-10-20","arxiv_id":"2210.10964","repositories_listed":0,"syntology":null},{"url":null,"slug":"locally-smoothed-gaussian-process-regression","title":"Locally Smoothed Gaussian Process Regression","date":"2022-10-18","arxiv_id":"2210.09998","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditional-neural-processes-for-molecules","title":"Conditional Neural Processes for Molecules","date":"2022-10-17","arxiv_id":"2210.09211","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-of-rough-surfaces-with-gaussian","title":"Model of rough surfaces with Gaussian processes","date":"2022-10-15","arxiv_id":"2210.08279","repositories_listed":0,"syntology":null},{"url":null,"slug":"monotonicity-and-double-descent-in","title":"Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes","date":"2022-10-14","arxiv_id":"2210.07612","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-processes-on-distributions-based-on","title":"Gaussian Processes on Distributions based on Regularized Optimal Transport","date":"2022-10-12","arxiv_id":"2210.06574","repositories_listed":0,"syntology":null},{"url":null,"slug":"inference-on-causal-effects-of-interventions","title":"Inference on Causal Effects of Interventions in Time using Gaussian Processes","date":"2022-10-06","arxiv_id":"2210.02850","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-for-regression-with","title":"Active Learning for Regression with Aggregated Outputs","date":"2022-10-04","arxiv_id":"2210.01329","repositories_listed":0,"syntology":null},{"url":null,"slug":"safety-aware-learning-based-control-of","title":"Safety-Aware Learning-Based Control of Systems with Uncertainty Dependent Constraints (extended version)","date":"2022-10-04","arxiv_id":"2210.01374","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-knowledge-graph-completion-with","title":"Temporal Knowledge Graph Completion with Approximated Gaussian Process Embedding","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"physically-meaningful-uncertainty","title":"Physically Meaningful Uncertainty Quantification in Probabilistic Wind Turbine Power Curve Models as a Damage Sensitive Feature","date":"2022-09-30","arxiv_id":"2209.15579","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-stopping-with-gaussian-processes","title":"Optimal Stopping with Gaussian Processes","date":"2022-09-22","arxiv_id":"2209.14738","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-gaussian-process-hyperparameter","title":"Scalable Gaussian Process Hyperparameter Optimization via Coverage Regularization","date":"2022-09-22","arxiv_id":"2209.11280","repositories_listed":0,"syntology":null},{"url":null,"slug":"partial-sequence-labeling-with-structured","title":"Partial sequence labeling with structured Gaussian Processes","date":"2022-09-20","arxiv_id":"2209.09397","repositories_listed":0,"syntology":null},{"url":null,"slug":"interrelation-of-equivariant-gaussian","title":"Interrelation of equivariant Gaussian processes and convolutional neural networks","date":"2022-09-17","arxiv_id":"2209.08371","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-non-parametric-point-process-model-for","title":"Causal Modeling of Policy Interventions From Sequences of Treatments and Outcomes","date":"2022-09-09","arxiv_id":"2209.04142","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-sensor-placement-in-body-surface","title":"Optimal Sensor Placement in Body Surface Networks using Gaussian Processes","date":"2022-09-07","arxiv_id":"2209.02912","repositories_listed":0,"syntology":null},{"url":null,"slug":"log-gaussian-processes-for-ai-assisted-tas","title":"Active learning-assisted neutron spectroscopy with log-Gaussian processes","date":"2022-09-02","arxiv_id":"2209.00980","repositories_listed":0,"syntology":null},{"url":null,"slug":"bezier-gaussian-processes-for-tall-and-wide","title":"Bézier Gaussian Processes for Tall and Wide Data","date":"2022-09-01","arxiv_id":"2209.00343","repositories_listed":0,"syntology":null},{"url":null,"slug":"constraining-gaussian-processes-to-systems-of","title":"Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations","date":"2022-08-26","arxiv_id":"2208.12515","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixtures-of-gaussian-process-experts-with-smc","title":"Mixtures of Gaussian Process Experts with SMC$^2$","date":"2022-08-26","arxiv_id":"2208.12830","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-emulation-of-density-functional-theory","title":"Fast emulation of density functional theory simulations using approximate Gaussian processes","date":"2022-08-24","arxiv_id":"2208.11302","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-linear-modules-in-a-dynamic-network-1","title":"Learning linear modules in a dynamic network with missing node observations","date":"2022-08-23","arxiv_id":"2208.10995","repositories_listed":0,"syntology":null},{"url":null,"slug":"scale-invariant-process-regression","title":"Scale invariant process regression: Towards Bayesian ML with minimal assumptions","date":"2022-08-22","arxiv_id":"2208.10461","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-complementary-kernelized-learning","title":"Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Data","date":"2022-08-21","arxiv_id":"2208.09978","repositories_listed":0,"syntology":null},{"url":null,"slug":"modelling-spatio-temporal-trends-of-air","title":"Modelling spatio-temporal trends of air pollution in Africa","date":"2022-08-21","arxiv_id":"2208.12719","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-bayes-ai","title":"Quantum Bayesian Computation","date":"2022-08-17","arxiv_id":"2208.08068","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-process-surrogate-models-for-neural","title":"Gaussian Process Surrogate Models for Neural Networks","date":"2022-08-11","arxiv_id":"2208.06028","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-optimization-with-informative","title":"Bayesian Optimization with Informative Covariance","date":"2022-08-04","arxiv_id":"2208.02704","repositories_listed":0,"syntology":null},{"url":null,"slug":"approximate-bayesian-neural-operators","title":"Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs","date":"2022-08-02","arxiv_id":"2208.01565","repositories_listed":0,"syntology":null},{"url":null,"slug":"correcting-model-bias-with-sparse-implicit","title":"Correcting Model Bias with Sparse Implicit Processes","date":"2022-07-21","arxiv_id":"2207.10673","repositories_listed":0,"syntology":null},{"url":null,"slug":"kullback-leibler-and-renyi-divergences-in","title":"Kullback-Leibler and Renyi divergences in reproducing kernel Hilbert space and Gaussian process settings","date":"2022-07-18","arxiv_id":"2207.08406","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-inducing-points-and-uncertainty-on","title":"Learning inducing points and uncertainty on molecular data by scalable variational Gaussian processes","date":"2022-07-16","arxiv_id":"2207.07654","repositories_listed":0,"syntology":null}],"record_sha256":"e75e8a98a50f8192e3beab0e037c92b9a9f369a843b23da430644f5bf6ec6cf4","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}