{"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/ran/2","list_of":"/method/gaussian-process","method":"Gaussian Process","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not isolate this method inside it.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":2,"pages_in_order":2,"rows_per_page":100,"rows":[101,155],"of":155,"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/papers/ran/1","prev":"/method/gaussian-process/papers/ran/1","next":null,"papers":[{"paper":"/paper/boss-bayesian-optimization-over-string-spaces","slug":"boss-bayesian-optimization-over-string-spaces","title":"BOSS: Bayesian Optimization over String Spaces","date":"2020-10-02","arxiv_id":"2010.00979","n_code_links":1,"syntology":{"ran":8,"of":9,"n_ran_checked":8,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["henrymoss/BOSS"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/improving-predictions-of-bayesian-neural","slug":"improving-predictions-of-bayesian-neural","title":"Improving predictions of Bayesian neural nets via local linearization","date":"2020-08-19","arxiv_id":"2008.08400","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["AlexImmer/BNN-predictions"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/state-space-expectation-propagation-efficient","slug":"state-space-expectation-propagation-efficient","title":"State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes","date":"2020-07-12","arxiv_id":"2007.05994","n_code_links":1,"syntology":{"ran":8,"of":10,"n_ran_checked":8,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["AaltoML/kalman-jax"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/simple-and-principled-uncertainty-estimation","slug":"simple-and-principled-uncertainty-estimation","title":"Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness","date":"2020-06-17","arxiv_id":"2006.10108","n_code_links":4,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["google/uncertainty-baselines"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/neural-architecture-search-using-bayesian","slug":"neural-architecture-search-using-bayesian","title":"Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels","date":"2020-06-13","arxiv_id":"2006.07556","n_code_links":1,"syntology":{"ran":15,"of":19,"n_ran_checked":14,"n_instrument":1,"unverified":4,"pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 1 honoured, 0 violated, 13 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","official":null}},{"paper":"/paper/optimal-transport-kernels-for-sequential-and","slug":"optimal-transport-kernels-for-sequential-and","title":"Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search","date":"2020-06-13","arxiv_id":"2006.07593","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":0,"phrase":"3 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; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ntienvu/TW_NAS"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/manifold-gplvms-for-discovering-non-euclidean","slug":"manifold-gplvms-for-discovering-non-euclidean","title":"Manifold GPLVMs for discovering non-Euclidean latent structure in neural data","date":"2020-06-12","arxiv_id":"2006.07429","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["tachukao/mgplvm-pytorch"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/variational-auto-regressive-gaussian","slug":"variational-auto-regressive-gaussian","title":"Variational Auto-Regressive Gaussian Processes for Continual Learning","date":"2020-06-09","arxiv_id":"2006.05468","n_code_links":1,"syntology":{"ran":10,"of":11,"n_ran_checked":3,"n_instrument":7,"unverified":1,"pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 7 where Syntology's instrument failed) · 1 unverified","official":{"repos":["uber-research/vargp"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/randomised-gaussian-process-upper-confidence","slug":"randomised-gaussian-process-upper-confidence","title":"Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation","date":"2020-06-08","arxiv_id":"2006.04296","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","official":{"repos":["jmaberk/RGPUCB"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/path-imputation-strategies-for-signature","slug":"path-imputation-strategies-for-signature","title":"Path Imputation Strategies for Signature Models of Irregular Time Series","date":"2020-05-25","arxiv_id":"2005.12359","n_code_links":2,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":0,"phrase":"3 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; 3 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/global-inducing-point-variational-posteriors","slug":"global-inducing-point-variational-posteriors","title":"Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes","date":"2020-05-17","arxiv_id":"2005.08140","n_code_links":1,"syntology":{"ran":6,"of":13,"n_ran_checked":4,"n_instrument":2,"unverified":7,"pointer_only":13,"phrase":"6 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 7 unverified","official":{"repos":["LaurenceA/bayesfunc"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":7,"ran_from_kinds":["official"]}}},{"paper":"/paper/active-preference-based-gaussian-process","slug":"active-preference-based-gaussian-process","title":"Active Preference-Based Gaussian Process Regression for Reward Learning","date":"2020-05-06","arxiv_id":"2005.02575","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":1,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["Stanford-ILIAD/active-preference-based-gpr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/safe-multi-agent-interaction-through-robust","slug":"safe-multi-agent-interaction-through-robust","title":"Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties","date":"2020-04-11","arxiv_id":"2004.05273","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["rcheng805/robust_cbf"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/imbalance-learning-for-variable-star","slug":"imbalance-learning-for-variable-star","title":"Imbalance Learning for Variable Star Classification","date":"2020-02-27","arxiv_id":"2002.12386","n_code_links":2,"syntology":{"ran":3,"of":4,"n_ran_checked":0,"n_instrument":3,"unverified":1,"pointer_only":4,"phrase":"3 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; 3 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/one-shot-bayes-opt-with-probabilistic","slug":"one-shot-bayes-opt-with-probabilistic","title":"Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits","date":"2020-02-06","arxiv_id":"2002.02518","n_code_links":2,"syntology":{"ran":5,"of":5,"n_ran_checked":3,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["jparkerholder/PB2"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/approximate-inference-for-fully-bayesian","slug":"approximate-inference-for-fully-bayesian","title":"Approximate Inference for Fully Bayesian Gaussian Process Regression","date":"2019-12-31","arxiv_id":"1912.13440","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 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; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/continual-multi-task-gaussian-processes","slug":"continual-multi-task-gaussian-processes","title":"Continual Multi-task Gaussian Processes","date":"2019-10-31","arxiv_id":"1911.00002","n_code_links":2,"syntology":{"ran":7,"of":8,"n_ran_checked":3,"n_instrument":4,"unverified":1,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","official":{"repos":["pmorenoz/ContinualGP"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/function-space-distributions-over-kernels","slug":"function-space-distributions-over-kernels","title":"Function-Space Distributions over Kernels","date":"2019-10-29","arxiv_id":"1910.13565","n_code_links":1,"syntology":{"ran":3,"of":7,"n_ran_checked":3,"n_instrument":0,"unverified":4,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["wjmaddox/spectralgp"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/bananas-bayesian-optimization-with-neural","slug":"bananas-bayesian-optimization-with-neural","title":"BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search","date":"2019-10-25","arxiv_id":"1910.11858","n_code_links":3,"syntology":{"ran":6,"of":8,"n_ran_checked":6,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["naszilla/bananas","naszilla/naszilla"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/chembo-bayesian-optimization-of-small-organic","slug":"chembo-bayesian-optimization-of-small-organic","title":"ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations","date":"2019-08-05","arxiv_id":"1908.01425","n_code_links":1,"syntology":{"ran":4,"of":6,"n_ran_checked":4,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":"/paper/sequential-learning-of-active-subspaces","slug":"sequential-learning-of-active-subspaces","title":"Sequential Learning of Active Subspaces","date":"2019-07-26","arxiv_id":"1907.11572","n_code_links":1,"syntology":{"ran":6,"of":6,"n_ran_checked":6,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/bayesian-optimisation-over-multiple","slug":"bayesian-optimisation-over-multiple","title":"Bayesian Optimisation over Multiple Continuous and Categorical Inputs","date":"2019-06-20","arxiv_id":"1906.08878","n_code_links":2,"syntology":{"ran":7,"of":7,"n_ran_checked":7,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["rubinxin/CoCaBO_code"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/variational-gaussian-processes-with-signature","slug":"variational-gaussian-processes-with-signature","title":"Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances","date":"2019-06-19","arxiv_id":"1906.08215","n_code_links":1,"syntology":{"ran":5,"of":6,"n_ran_checked":5,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["tgcsaba/GPSig"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/expressive-priors-in-bayesian-neural-networks","slug":"expressive-priors-in-bayesian-neural-networks","title":"Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions","date":"2019-05-15","arxiv_id":"1905.06076","n_code_links":1,"syntology":{"ran":6,"of":9,"n_ran_checked":6,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":"/paper/knowing-the-what-but-not-the-where-in","slug":"knowing-the-what-but-not-the-where-in","title":"Knowing The What But Not The Where in Bayesian Optimization","date":"2019-05-07","arxiv_id":"1905.02685","n_code_links":1,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 1 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["ntienvu/KnownOptimum_BO"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/multi-view-stereo-by-temporal-nonparametric","slug":"multi-view-stereo-by-temporal-nonparametric","title":"Multi-View Stereo by Temporal Nonparametric Fusion","date":"2019-04-12","arxiv_id":"1904.06397","n_code_links":1,"syntology":{"ran":7,"of":8,"n_ran_checked":6,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/rates-of-convergence-for-sparse-variational","slug":"rates-of-convergence-for-sparse-variational","title":"Rates of Convergence for Sparse Variational Gaussian Process Regression","date":"2019-03-08","arxiv_id":"1903.03571","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/functional-regularisation-for-continual","slug":"functional-regularisation-for-continual","title":"Functional Regularisation for Continual Learning with Gaussian Processes","date":"2019-01-31","arxiv_id":"1901.11356","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":3,"phrase":"3 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; 3 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/gaussian-processes-with-linear-operator","slug":"gaussian-processes-with-linear-operator","title":"Gaussian processes with linear operator inequality constraints","date":"2019-01-10","arxiv_id":"1901.03134","n_code_links":2,"syntology":{"ran":8,"of":8,"n_ran_checked":8,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["cagrell/gp_constr"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/neural-non-stationary-spectral-kernel","slug":"neural-non-stationary-spectral-kernel","title":"Neural Non-Stationary Spectral Kernel","date":"2018-11-27","arxiv_id":"1811.10978","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["sremes/nssm-gp"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/hyperparameter-learning-via-distributional","slug":"hyperparameter-learning-via-distributional","title":"Hyperparameter Learning via Distributional Transfer","date":"2018-10-15","arxiv_id":"1810.06305","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":0,"n_instrument":1,"unverified":2,"pointer_only":3,"phrase":"1 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; 1 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":"/paper/probabilistic-solutions-to-ordinary","slug":"probabilistic-solutions-to-ordinary","title":"Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspective","date":"2018-10-08","arxiv_id":"1810.03440","n_code_links":1,"syntology":{"ran":10,"of":10,"n_ran_checked":10,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/deep-convolutional-gaussian-processes","slug":"deep-convolutional-gaussian-processes","title":"Deep convolutional Gaussian processes","date":"2018-10-06","arxiv_id":"1810.03052","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["kekeblom/DeepCGP"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/gpdoemd-a-python-package-for-design-of","slug":"gpdoemd-a-python-package-for-design-of","title":"GPdoemd: a Python package for design of experiments for model discrimination","date":"2018-10-05","arxiv_id":"1810.02561","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":1,"n_instrument":3,"unverified":0,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["cog-imperial/GPdoemd"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/set-transformer-a-framework-for-attention","slug":"set-transformer-a-framework-for-attention","title":"Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks","date":"2018-10-01","arxiv_id":"1810.00825","n_code_links":9,"syntology":{"ran":8,"of":9,"n_ran_checked":6,"n_instrument":2,"unverified":1,"pointer_only":4,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 3 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["juho-lee/set_transformer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["listed","official","unlocated"]}}},{"paper":"/paper/causal-inference-and-mechanism-clustering-of","slug":"causal-inference-and-mechanism-clustering-of","title":"Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models","date":"2018-09-23","arxiv_id":"1809.08568","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"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) · 0 unverified","official":{"repos":["amber0309/ANM-MM"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/deep-convolutional-networks-as-shallow","slug":"deep-convolutional-networks-as-shallow","title":"Deep Convolutional Networks as shallow Gaussian Processes","date":"2018-08-16","arxiv_id":"1808.05587","n_code_links":3,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["convnets-as-gps/convnets-as-gps","rhaps0dy/convnets-as-gps"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/a-tutorial-on-bayesian-optimization","slug":"a-tutorial-on-bayesian-optimization","title":"A Tutorial on Bayesian Optimization","date":"2018-07-08","arxiv_id":"1807.02811","n_code_links":7,"syntology":{"ran":9,"of":10,"n_ran_checked":8,"n_instrument":1,"unverified":1,"pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 1 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["wujian16/Cornell-MOE"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/neural-processes","slug":"neural-processes","title":"Neural Processes","date":"2018-07-04","arxiv_id":"1807.01622","n_code_links":14,"syntology":{"ran":11,"of":12,"n_ran_checked":10,"n_instrument":1,"unverified":1,"pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["deepmind/neural-processes"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/dirichlet-based-gaussian-processes-for-large","slug":"dirichlet-based-gaussian-processes-for-large","title":"Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification","date":"2018-05-28","arxiv_id":"1805.10915","n_code_links":1,"syntology":{"ran":8,"of":8,"n_ran_checked":8,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["dmilios/dirichletGPC"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/heterogeneous-multi-output-gaussian-process","slug":"heterogeneous-multi-output-gaussian-process","title":"Heterogeneous Multi-output Gaussian Process Prediction","date":"2018-05-19","arxiv_id":"1805.07633","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":{"repos":["pmorenoz/HetMOGP"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/gaussian-process-behaviour-in-wide-deep","slug":"gaussian-process-behaviour-in-wide-deep","title":"Gaussian Process Behaviour in Wide Deep Neural Networks","date":"2018-04-30","arxiv_id":"1804.11271","n_code_links":2,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["widedeepnetworks/widedeepnetworks"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"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":"/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":"/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":"/paper/correcting-boundary-over-exploration","slug":"correcting-boundary-over-exploration","title":"Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations","date":"2017-04-04","arxiv_id":"1704.00963","n_code_links":1,"syntology":{"ran":2,"of":4,"n_ran_checked":1,"n_instrument":1,"unverified":2,"pointer_only":4,"phrase":"2 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; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["esiivola/vdsobo"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/adaptive-gaussian-process-approximation-for","slug":"adaptive-gaussian-process-approximation-for","title":"Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions","date":"2017-03-29","arxiv_id":"1703.09930","n_code_links":1,"syntology":{"ran":13,"of":15,"n_ran_checked":13,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":"/paper/modeling-long-and-short-term-temporal","slug":"modeling-long-and-short-term-temporal","title":"Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks","date":"2017-03-21","arxiv_id":"1703.07015","n_code_links":21,"syntology":{"ran":7,"of":7,"n_ran_checked":7,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["laiguokun/LSTNet","laiguokun/multivariate-time-series-data"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/a-scalable-end-to-end-gaussian-process","slug":"a-scalable-end-to-end-gaussian-process","title":"A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification","date":"2016-06-14","arxiv_id":"1606.04443","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/bayesian-optimization-with-safety-constraints","slug":"bayesian-optimization-with-safety-constraints","title":"Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics","date":"2016-02-14","arxiv_id":"1602.04450","n_code_links":3,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["befelix/SafeOpt"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/probabilistic-line-searches-for-stochastic-1","slug":"probabilistic-line-searches-for-stochastic-1","title":"Probabilistic Line Searches for Stochastic Optimization","date":"2015-02-10","arxiv_id":"1502.02846","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":"/paper/freeze-thaw-bayesian-optimization","slug":"freeze-thaw-bayesian-optimization","title":"Freeze-Thaw Bayesian Optimization","date":"2014-06-16","arxiv_id":"1406.3896","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":null}},{"paper":"/paper/gaussian-processes-for-big-data","slug":"gaussian-processes-for-big-data","title":"Gaussian Processes for Big Data","date":"2013-09-26","arxiv_id":"1309.6835","n_code_links":9,"syntology":{"ran":4,"of":6,"n_ran_checked":4,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":"/paper/gaussian-process-regression-networks","slug":"gaussian-process-regression-networks","title":"Gaussian Process Regression Networks","date":"2011-10-19","arxiv_id":"1110.4411","n_code_links":1,"syntology":{"ran":1,"of":7,"n_ran_checked":1,"n_instrument":0,"unverified":6,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","official":null}},{"paper":"/paper/gaussian-process-optimization-in-the-bandit","slug":"gaussian-process-optimization-in-the-bandit","title":"Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design","date":"2009-12-21","arxiv_id":"0912.3995","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 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; 0 where Syntology's instrument failed) · 0 unverified","official":null}}],"record_sha256":"c10f235f80473ff377a8abebe87fb6ad14e6f874a9caa317f605753b1b2fa3e2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}