{"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":"/method/gaussian-process/papers/22","list_of":"/method/gaussian-process","method":"Gaussian Process","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":22,"pages_in_order":25,"rows_per_page":100,"rows":[2101,2200],"of":2473,"counts":{"archive_papers_tagged":2473,"with_a_code_link":734,"where_syntology_ran_a_sample":155,"not_listed_spam_title":0,"listed":2473,"listed_where_code_ran":155,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":130,"every_run_a_failure_of_syntologys_instrument":25,"listed_with_a_run_with_no_instrument_failure":130,"listed_every_run_a_failure_of_syntologys_instrument":25,"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":"/method/gaussian-process","prev":"/method/gaussian-process/papers/21","next":"/method/gaussian-process/papers/23","papers":[{"paper":null,"slug":"persistent-monitoring-of-stochastic-spatio","title":"Persistent Monitoring of Stochastic Spatio-temporal Phenomena with a Small Team of Robots","date":"2018-04-27","arxiv_id":"1804.10544","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-manifolds-from-non-stationary","title":"Learning Manifolds from Non-stationary Streaming Data","date":"2018-04-24","arxiv_id":"1804.08833","n_code_links":0,"syntology":null},{"paper":null,"slug":"gaussian-material-synthesis","title":"Gaussian Material Synthesis","date":"2018-04-23","arxiv_id":"1804.08369","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-sequential-sampling-strategy-for-extreme","title":"A sequential sampling strategy for extreme event statistics in nonlinear dynamical systems","date":"2018-04-19","arxiv_id":"1804.07240","n_code_links":0,"syntology":null},{"paper":"/paper/fast-gaussian-process-based-gradient-matching","slug":"fast-gaussian-process-based-gradient-matching","title":"Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs","date":"2018-04-12","arxiv_id":"1804.04378","n_code_links":3,"syntology":null},{"paper":null,"slug":"scalable-magnetic-field-slam-in-3d-using","title":"Scalable Magnetic Field SLAM in 3D Using Gaussian Process Maps","date":"2018-04-05","arxiv_id":"1804.01926","n_code_links":0,"syntology":null},{"paper":null,"slug":"gaussian-process-uncertainty-in-age","title":"Gaussian Process Uncertainty in Age Estimation as a Measure of Brain Abnormality","date":"2018-04-04","arxiv_id":"1804.01296","n_code_links":0,"syntology":null},{"paper":null,"slug":"pseudo-marginal-bayesian-inference-for","title":"Pseudo-marginal Bayesian inference for supervised Gaussian process latent variable models","date":"2018-03-28","arxiv_id":"1803.10746","n_code_links":0,"syntology":null},{"paper":null,"slug":"distributed-adaptive-sampling-for-kernel","title":"Distributed Adaptive Sampling for Kernel Matrix Approximation","date":"2018-03-27","arxiv_id":"1803.10172","n_code_links":0,"syntology":null},{"paper":"/paper/learning-based-model-predictive-control-for","slug":"learning-based-model-predictive-control-for","title":"Learning-based Model Predictive Control for Safe Exploration","date":"2018-03-22","arxiv_id":"1803.08287","n_code_links":1,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":null,"slug":"a-feature-driven-active-framework-for","title":"A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation","date":"2018-03-20","arxiv_id":"1803.07682","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-multi-perspective-approach-to-anomaly","title":"A Multi-perspective Approach To Anomaly Detection For Self-aware Embodied Agents","date":"2018-03-17","arxiv_id":"1803.06579","n_code_links":0,"syntology":null},{"paper":"/paper/constant-time-predictive-distributions-for","slug":"constant-time-predictive-distributions-for","title":"Constant-Time Predictive Distributions for Gaussian Processes","date":"2018-03-16","arxiv_id":"1803.06058","n_code_links":1,"syntology":null},{"paper":"/paper/capturing-structure-implicitly-from-time","slug":"capturing-structure-implicitly-from-time","title":"Capturing Structure Implicitly from Time-Series having Limited Data","date":"2018-03-15","arxiv_id":"1803.05867","n_code_links":1,"syntology":null},{"paper":null,"slug":"gaussian-processes-over-graphs","title":"Gaussian Processes Over Graphs","date":"2018-03-15","arxiv_id":"1803.05776","n_code_links":0,"syntology":null},{"paper":"/paper/variational-zero-inflated-gaussian-processes","slug":"variational-zero-inflated-gaussian-processes","title":"Variational zero-inflated Gaussian processes with sparse kernels","date":"2018-03-13","arxiv_id":"1803.05036","n_code_links":1,"syntology":null},{"paper":"/paper/conditionally-independent-multiresolution","slug":"conditionally-independent-multiresolution","title":"Conditionally Independent Multiresolution Gaussian Processes","date":"2018-02-25","arxiv_id":"1802.09086","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-causally-generated-stationary-time","title":"Learning Causally-Generated Stationary Time Series","date":"2018-02-22","arxiv_id":"1802.08167","n_code_links":0,"syntology":null},{"paper":"/paper/personalized-gaussian-processes-for","slug":"personalized-gaussian-processes-for","title":"Personalized Gaussian Processes for Forecasting of Alzheimer's Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13)","date":"2018-02-22","arxiv_id":"1802.08561","n_code_links":1,"syntology":null},{"paper":null,"slug":"emulating-dynamic-non-linear-simulators-using","title":"Emulating dynamic non-linear simulators using Gaussian processes","date":"2018-02-21","arxiv_id":"1802.07575","n_code_links":0,"syntology":null},{"paper":"/paper/autoprognosis-automated-clinical-prognostic","slug":"autoprognosis-automated-clinical-prognostic","title":"AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning","date":"2018-02-20","arxiv_id":"1802.07207","n_code_links":1,"syntology":null},{"paper":null,"slug":"design-of-experiments-for-model","title":"Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches","date":"2018-02-12","arxiv_id":"1802.04170","n_code_links":0,"syntology":null},{"paper":null,"slug":"gaussian-process-classification-with","title":"Gaussian Process Classification with Privileged Information by Soft-to-Hard Labeling Transfer","date":"2018-02-12","arxiv_id":"1802.03877","n_code_links":0,"syntology":null},{"paper":null,"slug":"gaussian-process-landmarking-on-manifolds","title":"Gaussian Process Landmarking on Manifolds","date":"2018-02-09","arxiv_id":"1802.03479","n_code_links":0,"syntology":null},{"paper":null,"slug":"video-event-recognition-and-anomaly-detection","title":"Video Event Recognition and Anomaly Detection by Combining Gaussian Process and Hierarchical Dirichlet Process Models","date":"2018-02-09","arxiv_id":"1802.03257","n_code_links":0,"syntology":null},{"paper":"/paper/scalable-meta-learning-for-bayesian","slug":"scalable-meta-learning-for-bayesian","title":"Practical Transfer Learning for Bayesian Optimization","date":"2018-02-06","arxiv_id":"1802.02219","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["automl/transfer-hpo-framework"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"deep-learning-based-retinal-oct-segmentation","title":"Deep Learning based Retinal OCT Segmentation","date":"2018-01-29","arxiv_id":"1801.09749","n_code_links":0,"syntology":null},{"paper":null,"slug":"algorithmic-linearly-constrained-gaussian","title":"Algorithmic Linearly Constrained Gaussian Processes","date":"2018-01-28","arxiv_id":"1801.09197","n_code_links":0,"syntology":null},{"paper":null,"slug":"when-vehicles-see-pedestrians-with-phonesa","title":"When Vehicles See Pedestrians with Phones:A Multi-Cue Framework for Recognizing Phone-based Activities of Pedestrians","date":"2018-01-24","arxiv_id":"1801.08234","n_code_links":0,"syntology":null},{"paper":"/paper/variable-prioritization-in-nonlinear-black","slug":"variable-prioritization-in-nonlinear-black","title":"Variable Prioritization in Nonlinear Black Box Methods: A Genetic Association Case Study","date":"2018-01-22","arxiv_id":"1801.07318","n_code_links":1,"syntology":null},{"paper":null,"slug":"upgrading-from-gaussian-processes-to-students","title":"Upgrading from Gaussian Processes to Student's-T Processes","date":"2018-01-18","arxiv_id":"1801.06147","n_code_links":0,"syntology":null},{"paper":null,"slug":"implementation-of-deep-convolutional-neural","title":"Implementation of Deep Convolutional Neural Network in Multi-class Categorical Image Classification","date":"2018-01-03","arxiv_id":"1801.01397","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-to-treat-sepsis-with-multi-output","title":"Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks","date":"2018-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"network-scale-traffic-modeling-and","title":"Network-Scale Traffic Modeling and Forecasting with Graphical Lasso and Neural Networks","date":"2017-12-25","arxiv_id":"1801.00711","n_code_links":0,"syntology":null},{"paper":null,"slug":"estimating-activity-cycles-with-probabilistic-1","title":"Estimating activity cycles with probabilistic methods II. The Mount Wilson Ca H&K data","date":"2017-12-21","arxiv_id":"1712.08240","n_code_links":0,"syntology":null},{"paper":"/paper/variable-selection-for-gaussian-processes-via","slug":"variable-selection-for-gaussian-processes-via","title":"Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution","date":"2017-12-21","arxiv_id":"1712.08048","n_code_links":2,"syntology":null},{"paper":"/paper/safe-policy-search-with-gaussian-process","slug":"safe-policy-search-with-gaussian-process","title":"Safe Policy Search with Gaussian Process Models","date":"2017-12-15","arxiv_id":"1712.05556","n_code_links":1,"syntology":null},{"paper":null,"slug":"practical-bayesian-optimization-in-the","title":"Practical Bayesian optimization in the presence of outliers","date":"2017-12-12","arxiv_id":"1712.04567","n_code_links":0,"syntology":null},{"paper":null,"slug":"shape-optimization-in-laminar-flow-with-a","title":"Shape optimization in laminar flow with a label-guided variational autoencoder","date":"2017-12-10","arxiv_id":"1712.03599","n_code_links":0,"syntology":null},{"paper":null,"slug":"gaussian-process-regression-for-arctic","title":"Gaussian Process Regression for Arctic Coastal Erosion Forecasting","date":"2017-12-04","arxiv_id":"1712.00867","n_code_links":0,"syntology":null},{"paper":null,"slug":"cross-spectral-factor-analysis","title":"Cross-Spectral Factor Analysis","date":"2017-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"novel-exploration-techniques-nets-for-malaria","title":"Novel Exploration Techniques (NETs) for Malaria Policy Interventions","date":"2017-12-01","arxiv_id":"1712.00428","n_code_links":0,"syntology":null},{"paper":"/paper/personalized-gaussian-processes-for-future","slug":"personalized-gaussian-processes-for-future","title":"Personalized Gaussian Processes for Future Prediction of Alzheimer's Disease Progression","date":"2017-12-01","arxiv_id":"1712.00181","n_code_links":1,"syntology":null},{"paper":null,"slug":"targeting-eeglfp-synchrony-with-neural-nets","title":"Targeting EEG/LFP Synchrony with Neural Nets","date":"2017-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"variational-inference-via-chi-upper-bound","title":"Variational Inference via \\chi Upper Bound Minimization","date":"2017-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"a-neural-stochastic-volatility-model","title":"A Neural Stochastic Volatility Model","date":"2017-11-30","arxiv_id":"1712.00504","n_code_links":0,"syntology":null},{"paper":null,"slug":"scalable-synthesis-of-safety-certificates","title":"Scalable synthesis of safety certificates from data with application to learning-based control","date":"2017-11-30","arxiv_id":"1711.11417","n_code_links":0,"syntology":null},{"paper":"/paper/towards-personalized-modeling-of-the-female","slug":"towards-personalized-modeling-of-the-female","title":"Towards Personalized Modeling of the Female Hormonal Cycle: Experiments with Mechanistic Models and Gaussian Processes","date":"2017-11-30","arxiv_id":"1712.00117","n_code_links":1,"syntology":null},{"paper":null,"slug":"uncertainty-estimates-for-efficient-neural","title":"Uncertainty Estimates for Efficient Neural Network-based Dialogue Policy Optimisation","date":"2017-11-30","arxiv_id":"1711.11486","n_code_links":0,"syntology":null},{"paper":null,"slug":"gaussian-process-neurons-learn-stochastic","title":"Gaussian Process Neurons Learn Stochastic Activation Functions","date":"2017-11-29","arxiv_id":"1711.11059","n_code_links":0,"syntology":null},{"paper":"/paper/dependent-relevance-determination-for-smooth","slug":"dependent-relevance-determination-for-smooth","title":"Dependent relevance determination for smooth and structured sparse regression","date":"2017-11-28","arxiv_id":"1711.10058","n_code_links":1,"syntology":null},{"paper":null,"slug":"variational-inference-for-gaussian-process-2","title":"Variational Inference for Gaussian Process Models with Linear Complexity","date":"2017-11-28","arxiv_id":"1711.10127","n_code_links":0,"syntology":null},{"paper":null,"slug":"approximate-inference-based-motion-planning","title":"Approximate Inference-based Motion Planning by Learning and Exploiting Low-Dimensional Latent Variable Models","date":"2017-11-22","arxiv_id":"1711.08275","n_code_links":0,"syntology":null},{"paper":null,"slug":"decentralized-high-dimensional-bayesian","title":"Decentralized High-Dimensional Bayesian Optimization with Factor Graphs","date":"2017-11-19","arxiv_id":"1711.07033","n_code_links":0,"syntology":null},{"paper":null,"slug":"sequential-randomized-matrix-factorization","title":"Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization","date":"2017-11-19","arxiv_id":"1711.06989","n_code_links":0,"syntology":null},{"paper":null,"slug":"cautious-nmpc-with-gaussian-process-dynamics","title":"Cautious NMPC with Gaussian Process Dynamics for Autonomous Miniature Race Cars","date":"2017-11-17","arxiv_id":"1711.06586","n_code_links":0,"syntology":null},{"paper":null,"slug":"gaussian-process-decentralized-data-fusion","title":"Gaussian Process Decentralized Data Fusion Meets Transfer Learning in Large-Scale Distributed Cooperative Perception","date":"2017-11-16","arxiv_id":"1711.06064","n_code_links":0,"syntology":null},{"paper":null,"slug":"joint-gaussian-processes-for-biophysical","title":"Joint Gaussian Processes for Biophysical Parameter Retrieval","date":"2017-11-14","arxiv_id":"1711.05197","n_code_links":0,"syntology":null},{"paper":null,"slug":"structured-variational-inference-for-coupled","title":"Structured Variational Inference for Coupled Gaussian Processes","date":"2017-11-03","arxiv_id":"1711.01131","n_code_links":0,"syntology":null},{"paper":"/paper/deep-recurrent-gaussian-process-with","slug":"deep-recurrent-gaussian-process-with","title":"Deep Recurrent Gaussian Process with Variational Sparse Spectrum Approximation","date":"2017-11-02","arxiv_id":"1711.00799","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-kernels-over-strings-using-gaussian","title":"Learning Kernels over Strings using Gaussian Processes","date":"2017-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"stochastic-variational-inference-for-fully","title":"Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression","date":"2017-11-01","arxiv_id":"1711.00221","n_code_links":0,"syntology":null},{"paper":null,"slug":"auto-differentiating-linear-algebra","title":"Auto-Differentiating Linear Algebra","date":"2017-10-24","arxiv_id":"1710.08717","n_code_links":0,"syntology":null},{"paper":"/paper/finite-dimensional-gaussian-approximation","slug":"finite-dimensional-gaussian-approximation","title":"Finite-dimensional Gaussian approximation with linear inequality constraints","date":"2017-10-20","arxiv_id":"1710.07453","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-gaussian-covariance-network","title":"Deep Gaussian Covariance Network","date":"2017-10-17","arxiv_id":"1710.06202","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-fusion-of-lidar-and-wide-angle-camera","title":"Robust Fusion of LiDAR and Wide-Angle Camera Data for Autonomous Mobile Robots","date":"2017-10-17","arxiv_id":"1710.06230","n_code_links":0,"syntology":null},{"paper":null,"slug":"safe-learning-of-quadrotor-dynamics-using","title":"Safe Learning of Quadrotor Dynamics Using Barrier Certificates","date":"2017-10-16","arxiv_id":"1710.05472","n_code_links":0,"syntology":null},{"paper":null,"slug":"bayesian-alignments-of-warped-multi-output","title":"Bayesian Alignments of Warped Multi-Output Gaussian Processes","date":"2017-10-08","arxiv_id":"1710.02766","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-for-drug-overdose","title":"Machine Learning for Drug Overdose Surveillance","date":"2017-10-06","arxiv_id":"1710.02458","n_code_links":0,"syntology":null},{"paper":null,"slug":"iterative-machine-learning-for-precision","title":"Iterative Machine Learning for Precision Trajectory Tracking with Series Elastic Actuators","date":"2017-10-05","arxiv_id":"1710.09691","n_code_links":0,"syntology":null},{"paper":null,"slug":"remote-sensing-image-classification-with","title":"Remote Sensing Image Classification with Large Scale Gaussian Processes","date":"2017-10-02","arxiv_id":"1710.00575","n_code_links":0,"syntology":null},{"paper":null,"slug":"forecasting-of-commercial-sales-with-large","title":"Forecasting of commercial sales with large scale Gaussian Processes","date":"2017-09-16","arxiv_id":"1709.05548","n_code_links":0,"syntology":null},{"paper":null,"slug":"bayesian-optimisation-for-safe-navigation","title":"Bayesian Optimisation for Safe Navigation under Localisation Uncertainty","date":"2017-09-07","arxiv_id":"1709.02169","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-improved-multi-output-gaussian-process-rnn","title":"An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection","date":"2017-08-19","arxiv_id":"1708.05894","n_code_links":0,"syntology":null},{"paper":null,"slug":"frequentist-coverage-and-sup-norm-convergence","title":"Frequentist coverage and sup-norm convergence rate in Gaussian process regression","date":"2017-08-16","arxiv_id":"1708.04753","n_code_links":0,"syntology":null},{"paper":null,"slug":"deepfacelift-interpretable-personalized","title":"DeepFaceLIFT: Interpretable Personalized Models for Automatic Estimation of Self-Reported Pain","date":"2017-08-09","arxiv_id":"1708.04670","n_code_links":0,"syntology":null},{"paper":null,"slug":"distributed-batch-gaussian-process","title":"Distributed Batch Gaussian Process Optimization","date":"2017-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"high-dimensional-bayesian-optimization-with","title":"High Dimensional Bayesian Optimization with Elastic Gaussian Process","date":"2017-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"preferential-bayesian-optmization","title":"Preferential Bayesian Optmization","date":"2017-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"source-target-similarity-modelings-for-multi","title":"Source-Target Similarity Modelings for Multi-Source Transfer Gaussian Process Regression","date":"2017-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ssn_mlrg1-at-semeval-2017-task-4-sentiment","title":"SSN\\_MLRG1 at SemEval-2017 Task 4: Sentiment Analysis in Twitter Using Multi-Kernel Gaussian Process Classifier","date":"2017-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ssn_mlrg1-at-semeval-2017-task-5-fine-grained","title":"SSN\\_MLRG1 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel Gaussian Process Regression Model","date":"2017-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"sequential-design-of-experiments-to-estimate","title":"Sequential design of experiments to estimate a probability of exceeding a threshold in a multi-fidelity stochastic simulator","date":"2017-07-26","arxiv_id":"1707.08384","n_code_links":0,"syntology":null},{"paper":null,"slug":"recovering-latent-signals-from-a-mixture-of","title":"Recovering Latent Signals from a Mixture of Measurements using a Gaussian Process Prior","date":"2017-07-19","arxiv_id":"1707.05909","n_code_links":0,"syntology":null},{"paper":null,"slug":"latent-gaussian-process-regression","title":"Latent Gaussian Process Regression","date":"2017-07-18","arxiv_id":"1707.05534","n_code_links":0,"syntology":null},{"paper":"/paper/application-of-ssvgmm-to-medical-data","slug":"application-of-ssvgmm-to-medical-data","title":"Application of SsVGMM to medical data-classification with novelty detection","date":"2017-07-15","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"enhanced-particle-swarm-optimization","title":"Enhanced Particle Swarm Optimization Algorithms for Multiple-Input Multiple-Output System Modelling using Convolved Gaussian Process Models","date":"2017-07-12","arxiv_id":"1709.04319","n_code_links":0,"syntology":null},{"paper":null,"slug":"large-scale-variable-fidelity-surrogate","title":"Large Scale Variable Fidelity Surrogate Modeling","date":"2017-07-12","arxiv_id":"1707.03916","n_code_links":0,"syntology":null},{"paper":null,"slug":"incorporating-uncertainty-into-deep-learning","title":"Incorporating Uncertainty into Deep Learning for Spoken Language Assessment","date":"2017-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/data-efficient-reinforcement-learning-with","slug":"data-efficient-reinforcement-learning-with","title":"Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control","date":"2017-06-20","arxiv_id":"1706.06491","n_code_links":1,"syntology":null},{"paper":null,"slug":"user-driven-mobile-robot-storyboarding","title":"Rapid Probabilistic Interest Learning from Domain-Specific Pairwise Image Comparisons","date":"2017-06-19","arxiv_id":"1706.05850","n_code_links":0,"syntology":null},{"paper":null,"slug":"nudged-elastic-band-calculations-accelerated","title":"Nudged elastic band calculations accelerated with Gaussian process regression","date":"2017-06-14","arxiv_id":"1706.04606","n_code_links":0,"syntology":null},{"paper":"/paper/dealing-with-integer-valued-variables-in","slug":"dealing-with-integer-valued-variables-in","title":"Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian Processes","date":"2017-06-12","arxiv_id":"1706.03673","n_code_links":1,"syntology":null},{"paper":null,"slug":"multiple-kernel-learning-and-automatic","title":"Multiple Kernel Learning and Automatic Subspace Relevance Determination for High-dimensional Neuroimaging Data","date":"2017-06-02","arxiv_id":"1706.00856","n_code_links":0,"syntology":null},{"paper":null,"slug":"identification-of-gaussian-process-state-1","title":"Identification of Gaussian Process State Space Models","date":"2017-05-30","arxiv_id":"1705.10888","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-modeling-of-latent-information-in","slug":"efficient-modeling-of-latent-information-in","title":"Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes","date":"2017-05-27","arxiv_id":"1705.09862","n_code_links":2,"syntology":null},{"paper":"/paper/non-stationary-spectral-kernels","slug":"non-stationary-spectral-kernels","title":"Non-Stationary Spectral Kernels","date":"2017-05-24","arxiv_id":"1705.08736","n_code_links":1,"syntology":null},{"paper":null,"slug":"individualized-risk-prognosis-for-critical","title":"Individualized Risk Prognosis for Critical Care Patients: A Multi-task Gaussian Process Model","date":"2017-05-22","arxiv_id":"1705.07674","n_code_links":0,"syntology":null},{"paper":"/paper/streaming-sparse-gaussian-process","slug":"streaming-sparse-gaussian-process","title":"Streaming Sparse Gaussian Process Approximations","date":"2017-05-19","arxiv_id":"1705.07131","n_code_links":3,"syntology":{"ran":3,"of":6,"n_ran_checked":1,"n_instrument":2,"unverified":3,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","official":{"repos":["thangbui/streaming_sparse_gp"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"analysis-of-thompson-sampling-for-gaussian","title":"Adaptive Rate of Convergence of Thompson Sampling for Gaussian Process Optimization","date":"2017-05-18","arxiv_id":"1705.06808","n_code_links":0,"syntology":null}],"record_sha256":"ed4bda832e4cf3c2a43dc31faa89db52e81c487b8f94240263914006ea017109","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}