{"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/12","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":12,"pages_in_order":20,"rows_per_page":100,"rows":[1101,1200],"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/11","next":"/task/gaussian-processes/papers/13","papers":[{"url":null,"slug":"comparative-analysis-of-time-series","title":"Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction","date":"2022-07-03","arxiv_id":"2207.01019","repositories_listed":0,"syntology":null},{"url":null,"slug":"infinite-fidelity-coregionalization-for","title":"Infinite-Fidelity Coregionalization for Physical Simulation","date":"2022-07-01","arxiv_id":"2207.00678","repositories_listed":0,"syntology":null},{"url":null,"slug":"off-the-grid-learning-of-sparse-mixtures-from","title":"Off-the-grid learning of mixtures from a continuous dictionary","date":"2022-06-29","arxiv_id":"2207.00171","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-renyi-cross-entropy","title":"On the Rényi Cross-Entropy","date":"2022-06-28","arxiv_id":"2206.14329","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributional-gaussian-processes-layers-for","title":"Distributional Gaussian Processes Layers for Out-of-Distribution Detection","date":"2022-06-27","arxiv_id":"2206.13346","repositories_listed":0,"syntology":null},{"url":null,"slug":"aggregated-multi-output-gaussian-processes","title":"Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains","date":"2022-06-24","arxiv_id":"2206.12141","repositories_listed":0,"syntology":null},{"url":null,"slug":"physically-consistent-learning-of","title":"Physically Consistent Learning of Conservative Lagrangian Systems with Gaussian Processes","date":"2022-06-24","arxiv_id":"2206.12272","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generalised-form-for-a-homogeneous","title":"A generalised form for a homogeneous population of structures using an overlapping mixture of Gaussian processes","date":"2022-06-23","arxiv_id":"2206.11683","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-kernel-gaussian-processes-through","title":"Sparse Kernel Gaussian Processes through Iterative Charted Refinement (ICR)","date":"2022-06-21","arxiv_id":"2206.10634","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-integrating-prior-knowledge-into-gaussian","title":"On Integrating Prior Knowledge into Gaussian Processes for Prognostic Health Monitoring","date":"2022-06-17","arxiv_id":"2206.08600","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-circuit-sizing-with-multi-objective","title":"Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference","date":"2022-06-06","arxiv_id":"2206.02391","repositories_listed":0,"syntology":null},{"url":null,"slug":"information-theoretic-inducing-point","title":"Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation","date":"2022-06-06","arxiv_id":"2206.02437","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-deep-learning-for-spatial-and","title":"Statistical Deep Learning for Spatial and Spatio-Temporal Data","date":"2022-06-05","arxiv_id":"2206.02218","repositories_listed":0,"syntology":null},{"url":null,"slug":"constraining-gaussian-processes-for-physics","title":"Constraining Gaussian processes for physics-informed acoustic emission mapping","date":"2022-06-03","arxiv_id":"2206.01495","repositories_listed":0,"syntology":null},{"url":null,"slug":"lessons-learned-from-data-driven-building","title":"Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning","date":"2022-05-31","arxiv_id":"2205.15703","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-active-learning-for-scanning-probe","title":"Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning","date":"2022-05-30","arxiv_id":"2205.15458","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-transformed-gaussian-processes-for","title":"Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification","date":"2022-05-30","arxiv_id":"2205.15008","repositories_listed":0,"syntology":null},{"url":null,"slug":"predictive-rate-selection-for-ultra-reliable","title":"Predictive Rate Selection for Ultra-Reliable Communication using Statistical Radio Maps","date":"2022-05-30","arxiv_id":"2205.15030","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-disagreement-in-automatic-data","title":"Modeling Disagreement in Automatic Data Labelling for Semi-Supervised Learning in Clinical Natural Language Processing","date":"2022-05-29","arxiv_id":"2205.14761","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-bayesian-learning-for-data","title":"Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling","date":"2022-05-28","arxiv_id":"2205.14283","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-gaussian-process-based","title":"Distributed Gaussian Process Based Cooperative Visual Pursuit Control for Drone Networks","date":"2022-05-27","arxiv_id":"2205.13714","repositories_listed":0,"syntology":null},{"url":null,"slug":"sample-efficient-optimisation-with","title":"Sample-Efficient Optimisation with Probabilistic Transformer Surrogates","date":"2022-05-27","arxiv_id":"2205.13902","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-variable-selection-makes-scalable","title":"Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes","date":"2022-05-26","arxiv_id":"2205.13676","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-black-and-gray-box-chemotactic-pdes","title":"Learning black- and gray-box chemotactic PDEs/closures from agent based Monte Carlo simulation data","date":"2022-05-26","arxiv_id":"2205.13545","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-gaussian-process-posterior-mean","title":"Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation","date":"2022-05-22","arxiv_id":"2205.10879","repositories_listed":0,"syntology":null},{"url":null,"slug":"exact-gaussian-processes-for-massive-datasets","title":"Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels","date":"2022-05-18","arxiv_id":"2205.09070","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-application-of-scenario-exploration-to","title":"An Application of Scenario Exploration to Find New Scenarios for the Development and Testing of Automated Driving Systems in Urban Scenarios","date":"2022-05-17","arxiv_id":"2205.08202","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-variational-smoothed-model","title":"Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checking","date":"2022-05-11","arxiv_id":"2205.05398","repositories_listed":0,"syntology":null},{"url":null,"slug":"designing-robust-biotechnological-processes","title":"Designing Robust Biotechnological Processes Regarding Variabilities using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design","date":"2022-05-06","arxiv_id":"2205.03261","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-boundary-conditions-parametrized-by","title":"On boundary conditions parametrized by analytic functions","date":"2022-05-06","arxiv_id":"2205.03185","repositories_listed":0,"syntology":null},{"url":null,"slug":"bezier-curve-gaussian-processes","title":"Bézier Curve Gaussian Processes","date":"2022-05-03","arxiv_id":"2205.01754","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-models-for-manufacturing-lead","title":"Probabilistic Models for Manufacturing Lead Times","date":"2022-04-28","arxiv_id":"2204.13792","repositories_listed":0,"syntology":null},{"url":null,"slug":"know-thy-student-interactive-learning-with","title":"Know Thy Student: Interactive Learning with Gaussian Processes","date":"2022-04-26","arxiv_id":"2204.12072","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-gaussian-process-extrapolation-for-bart","title":"Local Gaussian process extrapolation for BART models with applications to causal inference","date":"2022-04-23","arxiv_id":"2204.10963","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-restoration-of-weather-affected","title":"Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN","date":"2022-04-23","arxiv_id":"2204.10970","repositories_listed":0,"syntology":null},{"url":null,"slug":"inducing-gaussian-process-networks","title":"Inducing Gaussian Process Networks","date":"2022-04-21","arxiv_id":"2204.09889","repositories_listed":0,"syntology":null},{"url":null,"slug":"pagp-a-physics-assisted-gaussian-process","title":"PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations","date":"2022-04-06","arxiv_id":"2204.02583","repositories_listed":0,"syntology":null},{"url":null,"slug":"discovering-and-forecasting-extreme-events","title":"Discovering and forecasting extreme events via active learning in neural operators","date":"2022-04-05","arxiv_id":"2204.02488","repositories_listed":0,"syntology":null},{"url":null,"slug":"inspire-distributed-bayesian-optimization-for","title":"INSPIRE: Distributed Bayesian Optimization for ImproviNg SPatIal REuse in Dense WLANs","date":"2022-03-30","arxiv_id":"2204.10184","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-control-barrier-functions-a-non","title":"Gaussian Control Barrier Functions : A Non-Parametric Paradigm to Safety","date":"2022-03-29","arxiv_id":"2203.15474","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-registration-for-gaussian","title":"Probabilistic Registration for Gaussian Process 3D shape modelling in the presence of extensive missing data","date":"2022-03-26","arxiv_id":"2203.14113","repositories_listed":0,"syntology":null},{"url":null,"slug":"position-tracking-using-likelihood-modeling","title":"Position Tracking using Likelihood Modeling of Channel Features with Gaussian Processes","date":"2022-03-24","arxiv_id":"2203.13110","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bayesian-approach-for-shaft-centre","title":"A Bayesian Approach for Shaft Centre Localisation in Journal Bearings","date":"2022-03-22","arxiv_id":"2203.11719","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-model-based-multi-agent","title":"Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation","date":"2022-03-14","arxiv_id":"2203.07322","repositories_listed":0,"syntology":null},{"url":null,"slug":"modelling-variability-in-vibration-based","title":"Modelling variability in vibration-based PBSHM via a generalised population form","date":"2022-03-14","arxiv_id":"2203.07115","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-connecting-deep-trigonometric-networks","title":"On Connecting Deep Trigonometric Networks with Deep Gaussian Processes: Covariance, Expressivity, and Neural Tangent Kernel","date":"2022-03-14","arxiv_id":"2203.07411","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-nash-equilibrium-of-moment-matching","title":"On the Nash equilibrium of moment-matching GANs for stationary Gaussian processes","date":"2022-03-14","arxiv_id":"2203.07136","repositories_listed":0,"syntology":null},{"url":null,"slug":"structure-and-distribution-metric-for","title":"Structure and Distribution Metric for Quantifying the Quality of Uncertainty: Assessing Gaussian Processes, Deep Neural Nets, and Deep Neural Operators for Regression","date":"2022-03-09","arxiv_id":"2203.04515","repositories_listed":0,"syntology":null},{"url":null,"slug":"second-life-lithium-ion-batteries-a-chemistry","title":"Evaluating feasibility of batteries for second-life applications using machine learning","date":"2022-03-08","arxiv_id":"2203.04249","repositories_listed":0,"syntology":null},{"url":null,"slug":"fully-decentralized-scalable-gaussian","title":"Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning","date":"2022-03-06","arxiv_id":"2203.02865","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-3d-generative-models-from-minimal","title":"Building 3D Generative Models from Minimal Data","date":"2022-03-04","arxiv_id":"2203.02554","repositories_listed":0,"syntology":null},{"url":null,"slug":"gpu-accelerated-policy-optimization-via-batch","title":"GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control","date":"2022-02-28","arxiv_id":"2202.13638","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalised-gaussian-process-latent-variable","title":"Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference","date":"2022-02-25","arxiv_id":"2202.12979","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-based-fault-tolerant-control-for-an","title":"Learning-Based Fault-Tolerant Control for an Hexarotor with Model Uncertainty","date":"2022-02-25","arxiv_id":"2202.12736","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-invariant-weights-in-neural-networks","title":"Learning Invariant Weights in Neural Networks","date":"2022-02-25","arxiv_id":"2202.12439","repositories_listed":0,"syntology":null},{"url":null,"slug":"networked-online-learning-for-control-of","title":"Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processes","date":"2022-02-23","arxiv_id":"2202.11491","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-processes-and-statistical-decision","title":"Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces","date":"2022-02-22","arxiv_id":"2202.10613","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-lifting-approach-to-learning-based-self","title":"A Lifting Approach to Learning-Based Self-Triggered Control with Gaussian Processes","date":"2022-02-21","arxiv_id":"2202.10174","repositories_listed":0,"syntology":null},{"url":null,"slug":"nonstationary-multi-output-gaussian-processes","title":"Nonstationary multi-output Gaussian processes via harmonizable spectral mixtures","date":"2022-02-18","arxiv_id":"2202.09233","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-inverter-control-by-learning-the-opf","title":"Fast Inverter Control by Learning the OPF Mapping using Sensitivity-Informed Gaussian Processes","date":"2022-02-15","arxiv_id":"2202.07500","repositories_listed":0,"syntology":null},{"url":null,"slug":"recovering-stochastic-dynamics-via-gaussian","title":"The Schrödinger Bridge between Gaussian Measures has a Closed Form","date":"2022-02-11","arxiv_id":"2202.05722","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-convergence-rates-for-sparse","title":"Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning","date":"2022-02-08","arxiv_id":"2202.04005","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-model-ensemble-analysis-with-neural","title":"Multi-model Ensemble Analysis with Neural Network Gaussian Processes","date":"2022-02-08","arxiv_id":"2202.04152","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-graphical-models-as-an-ensemble","title":"Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes","date":"2022-02-07","arxiv_id":"2202.03287","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-nearest-neighbor-gaussian","title":"Variational Nearest Neighbor Gaussian Process","date":"2022-02-03","arxiv_id":"2202.01694","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-kernel-based-approach-for-modelling","title":"A Kernel-Based Approach for Modelling Gaussian Processes with Functional Information","date":"2022-01-26","arxiv_id":"2201.11023","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-process-position-dependent","title":"Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder","date":"2022-01-19","arxiv_id":"2201.07511","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-time-series-anomaly-detection-with","title":"Online Time Series Anomaly Detection with State Space Gaussian Processes","date":"2022-01-18","arxiv_id":"2201.06763","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-visual-exploration-of-gaussian-processes","title":"A visual exploration of Gaussian Processes and Infinite Neural Networks","date":"2022-01-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-overview-of-uncertainty-quantification","title":"An Overview of Uncertainty Quantification Methods for Infinite Neural Networks","date":"2022-01-13","arxiv_id":"2201.04746","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-human-driver-interactions-using-an","title":"Modeling Human Driver Interactions Using an Infinite Policy Space Through Gaussian Processes","date":"2022-01-03","arxiv_id":"2201.01733","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-process-modeling-of-approximate","title":"Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders","date":"2022-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sum-of-squares-program-and-safe-learning-on","title":"Sum-of-Squares Program and Safe Learning On Maximizing the Region of Attraction of Partially Unknown Systems","date":"2022-01-01","arxiv_id":"2201.00137","repositories_listed":0,"syntology":null},{"url":null,"slug":"rough-multifactor-volatility-for-spx-and-vix","title":"Rough multifactor volatility for SPX and VIX options","date":"2021-12-28","arxiv_id":"2112.14310","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-based-methods-to-model-small-body","title":"Learning-based methods to model small body gravity fields for proximity operations: Safety and Robustness","date":"2021-12-18","arxiv_id":"2112.09998","repositories_listed":0,"syntology":null},{"url":null,"slug":"correlated-product-of-experts-for-sparse","title":"Correlated Product of Experts for Sparse Gaussian Process Regression","date":"2021-12-17","arxiv_id":"2112.09519","repositories_listed":0,"syntology":null},{"url":null,"slug":"experimental-data-driven-model-predictive","title":"Experimental Data-Driven Model Predictive Control of a Hospital HVAC System During Regular Use","date":"2021-12-14","arxiv_id":"2112.07323","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-rigidity-based-flocking-control-with","title":"Learning Rigidity-based Flocking Control with Gaussian Processes","date":"2021-12-14","arxiv_id":"2112.07779","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-advection-on-directed-graphs-using","title":"Modeling Advection on Directed Graphs using Matérn Gaussian Processes for Traffic Flow","date":"2021-12-14","arxiv_id":"2201.00001","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-sparse-expansion-for-deep-gaussian","title":"A Sparse Expansion For Deep Gaussian Processes","date":"2021-12-11","arxiv_id":"2112.05888","repositories_listed":0,"syntology":null},{"url":null,"slug":"structure-preserving-learning-using-gaussian","title":"Structure-Preserving Learning Using Gaussian Processes and Variational Integrators","date":"2021-12-10","arxiv_id":"2112.05451","repositories_listed":0,"syntology":null},{"url":null,"slug":"unified-field-theory-for-deep-and-recurrent","title":"Unified field theoretical approach to deep and recurrent neuronal networks","date":"2021-12-10","arxiv_id":"2112.05589","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-process-constraint-learning-for","title":"Gaussian Process Constraint Learning for Scalable Chance-Constrained Motion Planning from Demonstrations","date":"2021-12-08","arxiv_id":"2112.04612","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bayesian-take-on-option-pricing-with","title":"A Bayesian take on option pricing with Gaussian processes","date":"2021-12-07","arxiv_id":"2112.03718","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-fusion-with-latent-map-gaussian","title":"Data Fusion with Latent Map Gaussian Processes","date":"2021-12-04","arxiv_id":"2112.02206","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-gaussian-process-based-ground","title":"A Novel Gaussian Process Based Ground Segmentation Algorithm with Local-Smoothness Estimation","date":"2021-12-01","arxiv_id":"2112.05847","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-universal-probabilistic-spike-count-model","title":"A universal probabilistic spike count model reveals ongoing modulation of neural variability","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"continuous-time-edge-modelling-using-non","title":"Continuous-time edge modelling using non-parametric point processes","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-learn-dense-gaussian-processes","title":"Learning to Learn Dense Gaussian Processes for Few-Shot Learning","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mixed-neural-network-gaussian-processes","title":"Probability-Generating Function Kernels for Spherical Data","date":"2021-12-01","arxiv_id":"2112.00365","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-and-adaptive-temporal-difference","title":"Robust and Adaptive Temporal-Difference Learning Using An Ensemble of Gaussian Processes","date":"2021-12-01","arxiv_id":"2112.00882","repositories_listed":0,"syntology":null},{"url":null,"slug":"structure-aware-random-fourier-kernel-for","title":"Structure-Aware Random Fourier Kernel for Graphs","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"contextual-combinatorial-volatile-bandits","title":"Contextual Combinatorial Multi-output GP Bandits with Group Constraints","date":"2021-11-29","arxiv_id":"2111.14778","repositories_listed":0,"syntology":null},{"url":null,"slug":"dependence-between-bayesian-neural-network","title":"Dependence between Bayesian neural network units","date":"2021-11-29","arxiv_id":"2111.14397","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-fixed-b-limiting-distribution-and-the-erp","title":"The Fixed-b Limiting Distribution and the ERP of HAR Tests Under Nonstationarity","date":"2021-11-29","arxiv_id":"2111.14590","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-inverse-free-variational-bounds-for","title":"Improved Inverse-Free Variational Bounds for Sparse Gaussian Processes","date":"2021-11-22","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"non-separable-spatio-temporal-graph-kernels","title":"Non-separable Spatio-temporal Graph Kernels via SPDEs","date":"2021-11-16","arxiv_id":"2111.08524","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-knowledge-graph-embedding-based-on","title":"Temporal Knowledge Graph Embedding based on Multivariate Gaussian Process","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"safe-real-time-optimization-using-multi","title":"Safe Real-Time Optimization using Multi-Fidelity Gaussian Processes","date":"2021-11-10","arxiv_id":"2111.05589","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-bayesian-acquisition-functions-in","title":"Optimizing Bayesian acquisition functions in Gaussian Processes","date":"2021-11-09","arxiv_id":"2111.04930","repositories_listed":0,"syntology":null}],"record_sha256":"1a39af27589c447e6b6f582cf668a7e1e1806cb2293fd33e3da8b6e803e68f2e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}