{"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/uncertainty-quantification/papers/4","list_of":"/task/uncertainty-quantification","task":"Uncertainty Quantification","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":4,"pages_in_order":24,"rows_per_page":100,"rows":[301,400],"of":2366,"counts":{"archive_papers_tagged":2366,"with_a_code_link":832,"where_syntology_ran_a_sample":220,"not_listed_spam_title":0,"listed":2366,"listed_where_code_ran":220,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":179,"every_run_a_failure_of_syntologys_instrument":41,"listed_with_a_run_with_no_instrument_failure":179,"listed_every_run_a_failure_of_syntologys_instrument":41,"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/uncertainty-quantification","prev":"/task/uncertainty-quantification/papers/3","next":"/task/uncertainty-quantification/papers/5","papers":[{"url":"/paper/moment-based-parameter-inference-with-error","slug":"moment-based-parameter-inference-with-error","title":"Moment-based parameter inference with error guarantees for stochastic reaction networks","date":"2024-06-25","arxiv_id":"2406.17434","repositories_listed":1,"syntology":null},{"url":"/paper/conformal-time-series-decomposition-with","slug":"conformal-time-series-decomposition-with","title":"Conformal time series decomposition with component-wise exchangeability","date":"2024-06-24","arxiv_id":"2406.16766","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-entropy-probes-robust-and-cheap","slug":"semantic-entropy-probes-robust-and-cheap","title":"Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs","date":"2024-06-22","arxiv_id":"2406.15927","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/semantic-entropy-probes-robust-and-cheap#ran","syntology_url":"https://syntology.ai/paper/2406.15927","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.15927"}},"official":null}},{"url":"/paper/flat-posterior-does-matter-for-bayesian","slug":"flat-posterior-does-matter-for-bayesian","title":"Flat Posterior Does Matter For Bayesian Model Averaging","date":"2024-06-21","arxiv_id":"2406.15664","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":7,"phrase":"4 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; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/flat-posterior-does-matter-for-bayesian#ran","syntology_url":"https://syntology.ai/paper/2406.15664","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.15664"}},"official":{"repos":["mlai-yonsei/sa-bma"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/latentexplainer-explaining-latent","slug":"latentexplainer-explaining-latent","title":"LatentExplainer: Explaining Latent Representations in Deep Generative Models with Multimodal Large Language Models","date":"2024-06-21","arxiv_id":"2406.14862","repositories_listed":1,"syntology":null},{"url":"/paper/synchronous-faithfulness-monitoring-for","slug":"synchronous-faithfulness-monitoring-for","title":"Synchronous Faithfulness Monitoring for Trustworthy Retrieval-Augmented Generation","date":"2024-06-19","arxiv_id":"2406.13692","repositories_listed":1,"syntology":null},{"url":"/paper/a-variational-bayes-approach-to-debiased","slug":"a-variational-bayes-approach-to-debiased","title":"A variational Bayes approach to debiased inference for low-dimensional parameters in high-dimensional linear regression","date":"2024-06-18","arxiv_id":"2406.12659","repositories_listed":1,"syntology":null},{"url":"/paper/claudeslens-uncertainty-quantification-in","slug":"claudeslens-uncertainty-quantification-in","title":"ClaudesLens: Uncertainty Quantification in Computer Vision Models","date":"2024-06-18","arxiv_id":"2406.13008","repositories_listed":1,"syntology":null},{"url":"/paper/physics-constrained-learning-for-pde-systems","slug":"physics-constrained-learning-for-pde-systems","title":"Physics-Constrained Learning for PDE Systems with Uncertainty Quantified Port-Hamiltonian Models","date":"2024-06-17","arxiv_id":"2406.11809","repositories_listed":1,"syntology":null},{"url":"/paper/a-rate-distortion-view-of-uncertainty","slug":"a-rate-distortion-view-of-uncertainty","title":"A Rate-Distortion View of Uncertainty Quantification","date":"2024-06-16","arxiv_id":"2406.10775","repositories_listed":1,"syntology":null},{"url":"/paper/luma-a-benchmark-dataset-for-learning-from","slug":"luma-a-benchmark-dataset-for-learning-from","title":"LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal Data","date":"2024-06-14","arxiv_id":"2406.09864","repositories_listed":1,"syntology":null},{"url":"/paper/active-inference-meeting-energy-efficient","slug":"active-inference-meeting-energy-efficient","title":"Active Inference Meeting Energy-Efficient Control of Parallel and Identical Machines","date":"2024-06-13","arxiv_id":"2406.09322","repositories_listed":1,"syntology":null},{"url":"/paper/flexible-heteroscedastic-count-regression","slug":"flexible-heteroscedastic-count-regression","title":"Flexible Heteroscedastic Count Regression with Deep Double Poisson Networks","date":"2024-06-13","arxiv_id":"2406.09262","repositories_listed":1,"syntology":null},{"url":"/paper/conformal-prediction-for-class-wise-coverage","slug":"conformal-prediction-for-class-wise-coverage","title":"Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibration","date":"2024-06-10","arxiv_id":"2406.06818","repositories_listed":1,"syntology":{"n":24,"n_ran":21,"n_constructed":0,"n_ran_checked":20,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":20,"n_pointer_only":1,"phrase":"21 ran (of which 0 constructed an object rather than computing a result; 20 with no instrument failure: 0 honoured, 0 violated, 20 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/conformal-prediction-for-class-wise-coverage#ran","syntology_url":"https://syntology.ai/paper/2406.06818","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.06818"}},"official":{"repos":["yuanjiesh/rc3p"],"state":"official (archive's flag): 21 ran","n_ran":21,"n_constructed":0,"n_ran_no_instrument_failure":20,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/distribution-free-predictive-inference-under","slug":"distribution-free-predictive-inference-under","title":"Distribution-Free Predictive Inference under Unknown Temporal Drift","date":"2024-06-10","arxiv_id":"2406.06516","repositories_listed":1,"syntology":null},{"url":"/paper/on-subjective-uncertainty-quantification-and","slug":"on-subjective-uncertainty-quantification-and","title":"On Subjective Uncertainty Quantification and Calibration in Natural Language Generation","date":"2024-06-07","arxiv_id":"2406.05213","repositories_listed":1,"syntology":{"n":14,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/on-subjective-uncertainty-quantification-and#ran","syntology_url":"https://syntology.ai/paper/2406.05213","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.05213"}},"official":{"repos":["meta-inf/suq-nlg"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/winner-takes-all-learners-are-geometry-aware","slug":"winner-takes-all-learners-are-geometry-aware","title":"Winner-takes-all learners are geometry-aware conditional density estimators","date":"2024-06-07","arxiv_id":"2406.04706","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"5 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/winner-takes-all-learners-are-geometry-aware#ran","syntology_url":"https://syntology.ai/paper/2406.04706","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.04706"}},"official":{"repos":["Victorletzelter/VoronoiWTA"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/linear-opinion-pooling-for-uncertainty","slug":"linear-opinion-pooling-for-uncertainty","title":"Linear Opinion Pooling for Uncertainty Quantification on Graphs","date":"2024-06-06","arxiv_id":"2406.04041","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_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) · 0 unverified","sample_list":"/paper/linear-opinion-pooling-for-uncertainty#ran","syntology_url":"https://syntology.ai/paper/2406.04041","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.04041"}},"official":{"repos":["cortys/gpn-extensions"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/css-contrastive-semantic-similarity-for","slug":"css-contrastive-semantic-similarity-for","title":"CSS: Contrastive Semantic Similarity for Uncertainty Quantification of LLMs","date":"2024-06-05","arxiv_id":"2406.03158","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":11,"phrase":"9 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/css-contrastive-semantic-similarity-for#ran","syntology_url":"https://syntology.ai/paper/2406.03158","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.03158"}},"official":{"repos":["aoshuang92/css_uq_llms"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/relaxed-quantile-regression-prediction","slug":"relaxed-quantile-regression-prediction","title":"Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise","date":"2024-06-05","arxiv_id":"2406.03258","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":0,"n_instrument":6,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"6 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; 6 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/relaxed-quantile-regression-prediction#ran","syntology_url":"https://syntology.ai/paper/2406.03258","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.03258"}},"official":{"repos":["tpouplin/rqr"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-bayesian-approach-to-online-planning","slug":"a-bayesian-approach-to-online-planning","title":"A Bayesian Approach to Online Planning","date":"2024-06-04","arxiv_id":"2406.02103","repositories_listed":1,"syntology":null},{"url":"/paper/certifiably-byzantine-robust-federated","slug":"certifiably-byzantine-robust-federated","title":"Certifiably Byzantine-Robust Federated Conformal Prediction","date":"2024-06-04","arxiv_id":"2406.01960","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/certifiably-byzantine-robust-federated#ran","syntology_url":"https://syntology.ai/paper/2406.01960","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.01960"}},"official":{"repos":["kangmintong/rob-fcp"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/elastic-full-waveform-inversion-how-the","slug":"elastic-full-waveform-inversion-how-the","title":"Integrating Physics of the Problem into Data-Driven Methods to Enhance Elastic Full-Waveform Inversion with Uncertainty Quantification","date":"2024-06-04","arxiv_id":"2406.05153","repositories_listed":1,"syntology":null},{"url":"/paper/label-wise-aleatoric-and-epistemic","slug":"label-wise-aleatoric-and-epistemic","title":"Label-wise Aleatoric and Epistemic Uncertainty Quantification","date":"2024-06-04","arxiv_id":"2406.02354","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":14,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/label-wise-aleatoric-and-epistemic#ran","syntology_url":"https://syntology.ai/paper/2406.02354","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.02354"}},"official":{"repos":["YSale/label-uq"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/offline-bayesian-aleatoric-and-epistemic","slug":"offline-bayesian-aleatoric-and-epistemic","title":"Offline Bayesian Aleatoric and Epistemic Uncertainty Quantification and Posterior Value Optimisation in Finite-State MDPs","date":"2024-06-04","arxiv_id":"2406.02456","repositories_listed":1,"syntology":null},{"url":"/paper/the-deep-latent-space-particle-filter-for","slug":"the-deep-latent-space-particle-filter-for","title":"The Deep Latent Space Particle Filter for Real-Time Data Assimilation with Uncertainty Quantification","date":"2024-06-04","arxiv_id":"2406.02204","repositories_listed":1,"syntology":null},{"url":"/paper/robust-classification-by-coupling-data","slug":"robust-classification-by-coupling-data","title":"Robust Classification by Coupling Data Mollification with Label Smoothing","date":"2024-06-03","arxiv_id":"2406.01494","repositories_listed":1,"syntology":{"n":20,"n_ran":17,"n_constructed":0,"n_ran_checked":17,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":17,"n_pointer_only":20,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/robust-classification-by-coupling-data#ran","syntology_url":"https://syntology.ai/paper/2406.01494","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.01494"}},"official":{"repos":["markusheinonen/supervised-mollification"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":17,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/bayesian-joint-additive-factor-models-for","slug":"bayesian-joint-additive-factor-models-for","title":"Bayesian Joint Additive Factor Models for Multiview Learning","date":"2024-06-02","arxiv_id":"2406.00778","repositories_listed":1,"syntology":null},{"url":"/paper/reservoir-history-matching-of-the-norne-field","slug":"reservoir-history-matching-of-the-norne-field","title":"Reservoir History Matching of the Norne field with generative exotic priors and a coupled Mixture of Experts -- Physics Informed Neural Operator Forward Model","date":"2024-06-02","arxiv_id":"2406.00889","repositories_listed":1,"syntology":null},{"url":"/paper/streamflow-prediction-with-uncertainty","slug":"streamflow-prediction-with-uncertainty","title":"Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning Approach","date":"2024-05-31","arxiv_id":"2406.00133","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"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) · 0 unverified","sample_list":"/paper/streamflow-prediction-with-uncertainty#ran","syntology_url":"https://syntology.ai/paper/2406.00133","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.00133"}},"official":{"repos":["aminegha/streampred"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/veni-vindy-vici-a-variational-reduced-order","slug":"veni-vindy-vici-a-variational-reduced-order","title":"VENI, VINDy, VICI: a variational reduced-order modeling framework with uncertainty quantification","date":"2024-05-31","arxiv_id":"2405.20905","repositories_listed":1,"syntology":null},{"url":"/paper/conformal-depression-prediction","slug":"conformal-depression-prediction","title":"Conformal Depression Prediction","date":"2024-05-29","arxiv_id":"2405.18723","repositories_listed":1,"syntology":null},{"url":"/paper/valid-conformal-prediction-for-dynamic-gnns","slug":"valid-conformal-prediction-for-dynamic-gnns","title":"Valid Conformal Prediction for Dynamic GNNs","date":"2024-05-29","arxiv_id":"2405.19230","repositories_listed":1,"syntology":{"n":12,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":12,"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) · 6 unverified","sample_list":"/paper/valid-conformal-prediction-for-dynamic-gnns#ran","syntology_url":"https://syntology.ai/paper/2405.19230","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.19230"}},"official":{"repos":["edwarddavis1/valid_conformal_for_dynamic_gnn"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/verifiably-robust-conformal-prediction","slug":"verifiably-robust-conformal-prediction","title":"Verifiably Robust Conformal Prediction","date":"2024-05-29","arxiv_id":"2405.18942","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":2,"n_ran_checked":3,"n_instrument":5,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":10,"phrase":"8 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/verifiably-robust-conformal-prediction#ran","syntology_url":"https://syntology.ai/paper/2405.18942","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.18942"}},"official":{"repos":["ddv-lab/Verifiably_Robust_CP"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/bo4io-a-bayesian-optimization-approach-to","slug":"bo4io-a-bayesian-optimization-approach-to","title":"BO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantification","date":"2024-05-28","arxiv_id":"2405.17875","repositories_listed":1,"syntology":null},{"url":"/paper/semf-supervised-expectation-maximization","slug":"semf-supervised-expectation-maximization","title":"SEMF: Supervised Expectation-Maximization Framework for Predicting Intervals","date":"2024-05-28","arxiv_id":"2405.18176","repositories_listed":1,"syntology":null},{"url":"/paper/task-driven-uncertainty-quantification-in","slug":"task-driven-uncertainty-quantification-in","title":"Task-Driven Uncertainty Quantification in Inverse Problems via Conformal Prediction","date":"2024-05-28","arxiv_id":"2405.18527","repositories_listed":1,"syntology":null},{"url":"/paper/conformalized-late-fusion-multi-view-learning","slug":"conformalized-late-fusion-multi-view-learning","title":"Conformal Prediction for Ensembles: Improving Efficiency via Score-Based Aggregation","date":"2024-05-25","arxiv_id":"2405.16246","repositories_listed":1,"syntology":null},{"url":"/paper/prodag-projection-induced-variational","slug":"prodag-projection-induced-variational","title":"ProDAG: Projected Variational Inference for Directed Acyclic Graphs","date":"2024-05-24","arxiv_id":"2405.15167","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-density-uncertainty-quantification","slug":"semantic-density-uncertainty-quantification","title":"Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space","date":"2024-05-22","arxiv_id":"2405.13845","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":6,"phrase":"5 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; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/semantic-density-uncertainty-quantification#ran","syntology_url":"https://syntology.ai/paper/2405.13845","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.13845"}},"official":{"repos":["cognizant-ai-labs/semantic-density-paper"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-source-conformal-inference-under","slug":"multi-source-conformal-inference-under","title":"Multi-Source Conformal Inference Under Distribution Shift","date":"2024-05-15","arxiv_id":"2405.09331","repositories_listed":1,"syntology":null},{"url":"/paper/wasserstein-gradient-boosting-a-general","slug":"wasserstein-gradient-boosting-a-general","title":"Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning","date":"2024-05-15","arxiv_id":"2405.09536","repositories_listed":1,"syntology":null},{"url":"/paper/the-pitfalls-and-promise-of-conformal","slug":"the-pitfalls-and-promise-of-conformal","title":"The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks","date":"2024-05-14","arxiv_id":"2405.08886","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/the-pitfalls-and-promise-of-conformal#ran","syntology_url":"https://syntology.ai/paper/2405.08886","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.08886"}},"official":{"repos":["ziquanliu/ICML2024-AT-UR"],"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"]}}},{"url":"/paper/cvae-sm-a-conditional-variational-autoencoder","slug":"cvae-sm-a-conditional-variational-autoencoder","title":"CVAE-SM: A Conditional Variational Autoencoder with Style Modulation for Efficient Uncertainty Quantification","date":"2024-05-13","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/informativeness-of-weighted-conformal","slug":"informativeness-of-weighted-conformal","title":"Informativeness of Weighted Conformal Prediction","date":"2024-05-10","arxiv_id":"2405.06479","repositories_listed":1,"syntology":null},{"url":"/paper/aspire-iterative-amortized-posterior","slug":"aspire-iterative-amortized-posterior","title":"ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems","date":"2024-05-08","arxiv_id":"2405.05398","repositories_listed":1,"syntology":null},{"url":"/paper/accelerating-convergence-in-bayesian-few-shot","slug":"accelerating-convergence-in-bayesian-few-shot","title":"Accelerating Convergence in Bayesian Few-Shot Classification","date":"2024-05-02","arxiv_id":"2405.01507","repositories_listed":1,"syntology":null},{"url":"/paper/random-pareto-front-surfaces","slug":"random-pareto-front-surfaces","title":"Random Pareto front surfaces","date":"2024-05-02","arxiv_id":"2405.01404","repositories_listed":1,"syntology":null},{"url":"/paper/pessimistic-value-iteration-for-multi-task","slug":"pessimistic-value-iteration-for-multi-task","title":"Pessimistic Value Iteration for Multi-Task Data Sharing in Offline Reinforcement Learning","date":"2024-04-30","arxiv_id":"2404.19346","repositories_listed":1,"syntology":null},{"url":"/paper/predicting-safety-misbehaviours-in-autonomous","slug":"predicting-safety-misbehaviours-in-autonomous","title":"Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification","date":"2024-04-29","arxiv_id":"2404.18573","repositories_listed":1,"syntology":null},{"url":"/paper/implicit-generative-prior-for-bayesian-neural","slug":"implicit-generative-prior-for-bayesian-neural","title":"Implicit Generative Prior for Bayesian Neural Networks","date":"2024-04-27","arxiv_id":"2404.18008","repositories_listed":1,"syntology":null},{"url":"/paper/taming-false-positives-in-out-of-distribution","slug":"taming-false-positives-in-out-of-distribution","title":"Taming False Positives in Out-of-Distribution Detection with Human Feedback","date":"2024-04-25","arxiv_id":"2404.16954","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-catalyst-discovery-using","slug":"adaptive-catalyst-discovery-using","title":"Adaptive Catalyst Discovery Using Multicriteria Bayesian Optimization with Representation Learning","date":"2024-04-18","arxiv_id":"2404.12445","repositories_listed":1,"syntology":null},{"url":"/paper/information-theory-unifies-atomistic-machine","slug":"information-theory-unifies-atomistic-machine","title":"Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory","date":"2024-04-18","arxiv_id":"2404.12367","repositories_listed":1,"syntology":null},{"url":"/paper/physics-informed-active-learning-for","slug":"physics-informed-active-learning-for","title":"Physics-informed active learning for accelerating quantum chemical simulations","date":"2024-04-18","arxiv_id":"2404.11811","repositories_listed":1,"syntology":null},{"url":"/paper/a-variational-neural-bayes-framework-for","slug":"a-variational-neural-bayes-framework-for","title":"A variational neural Bayes framework for inference on intractable posterior distributions","date":"2024-04-16","arxiv_id":"2404.10899","repositories_listed":1,"syntology":null},{"url":"/paper/climode-climate-and-weather-forecasting-with","slug":"climode-climate-and-weather-forecasting-with","title":"ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs","date":"2024-04-15","arxiv_id":"2404.10024","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/climode-climate-and-weather-forecasting-with#ran","syntology_url":"https://syntology.ai/paper/2404.10024","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.10024"}},"official":{"repos":["aalto-quml/climode"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/leveraging-viscous-hamilton-jacobi-pdes-for","slug":"leveraging-viscous-hamilton-jacobi-pdes-for","title":"Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning","date":"2024-04-12","arxiv_id":"2404.08809","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-aware-tropical-cyclone-wind-speed","slug":"uncertainty-aware-tropical-cyclone-wind-speed","title":"Uncertainty Aware Tropical Cyclone Wind Speed Estimation from Satellite Data","date":"2024-04-12","arxiv_id":"2404.08325","repositories_listed":1,"syntology":null},{"url":"/paper/deep-generative-data-assimilation-in","slug":"deep-generative-data-assimilation-in","title":"Deep Generative Data Assimilation in Multimodal Setting","date":"2024-04-10","arxiv_id":"2404.06665","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deep-generative-data-assimilation-in#ran","syntology_url":"https://syntology.ai/paper/2404.06665","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.06665"}},"official":{"repos":["yongquan-qu/slams"],"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"]}}},{"url":"/paper/protein-property-prediction-with","slug":"protein-property-prediction-with","title":"Kermut: Composite kernel regression for protein variant effects","date":"2024-04-09","arxiv_id":"2407.00002","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_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) · 1 unverified","sample_list":"/paper/protein-property-prediction-with#ran","syntology_url":"https://syntology.ai/paper/2407.00002","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.00002"}},"official":{"repos":["petergroth/kermut"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/universal-functional-regression-with-neural","slug":"universal-functional-regression-with-neural","title":"Universal Functional Regression with Neural Operator Flows","date":"2024-04-03","arxiv_id":"2404.02986","repositories_listed":1,"syntology":null},{"url":"/paper/luq-long-text-uncertainty-quantification-for","slug":"luq-long-text-uncertainty-quantification-for","title":"LUQ: Long-text Uncertainty Quantification for LLMs","date":"2024-03-29","arxiv_id":"2403.20279","repositories_listed":1,"syntology":null},{"url":"/paper/data-adaptive-tradeoffs-among-multiple-risks","slug":"data-adaptive-tradeoffs-among-multiple-risks","title":"Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction","date":"2024-03-28","arxiv_id":"2403.19605","repositories_listed":1,"syntology":null},{"url":"/paper/on-uncertainty-quantification-for-near-bayes","slug":"on-uncertainty-quantification-for-near-bayes","title":"On Uncertainty Quantification for Near-Bayes Optimal Algorithms","date":"2024-03-28","arxiv_id":"2403.19381","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":9,"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) · 4 unverified","sample_list":"/paper/on-uncertainty-quantification-for-near-bayes#ran","syntology_url":"https://syntology.ai/paper/2403.19381","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.19381"}},"official":{"repos":["meta-inf/ipb"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/hierarchical-light-transformer-ensembles-for","slug":"hierarchical-light-transformer-ensembles-for","title":"Hierarchical Light Transformer Ensembles for Multimodal Trajectory Forecasting","date":"2024-03-26","arxiv_id":"2403.17678","repositories_listed":1,"syntology":null},{"url":"/paper/enabling-uncertainty-estimation-in-iterative","slug":"enabling-uncertainty-estimation-in-iterative","title":"Enabling Uncertainty Estimation in Iterative Neural Networks","date":"2024-03-25","arxiv_id":"2403.16732","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_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","sample_list":"/paper/enabling-uncertainty-estimation-in-iterative#ran","syntology_url":"https://syntology.ai/paper/2403.16732","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.16732"}},"official":{"repos":["cvlab-epfl/iter_unc"],"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"]}}},{"url":"/paper/an-analytic-solution-to-covariance","slug":"an-analytic-solution-to-covariance","title":"An Analytic Solution to Covariance Propagation in Neural Networks","date":"2024-03-24","arxiv_id":"2403.16163","repositories_listed":1,"syntology":null},{"url":"/paper/conformal-online-model-aggregation","slug":"conformal-online-model-aggregation","title":"Conformal online model aggregation","date":"2024-03-22","arxiv_id":"2403.15527","repositories_listed":1,"syntology":null},{"url":"/paper/hyperbolic-secant-representation-of-the","slug":"hyperbolic-secant-representation-of-the","title":"Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection","date":"2024-03-21","arxiv_id":"2403.14829","repositories_listed":1,"syntology":null},{"url":"/paper/an-ordering-of-divergences-for-variational","slug":"an-ordering-of-divergences-for-variational","title":"Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs","date":"2024-03-20","arxiv_id":"2403.13748","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-quantification-for-data-driven-2","slug":"uncertainty-quantification-for-data-driven-2","title":"Uncertainty quantification for data-driven weather models","date":"2024-03-20","arxiv_id":"2403.13458","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_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","sample_list":"/paper/uncertainty-quantification-for-data-driven-2#ran","syntology_url":"https://syntology.ai/paper/2403.13458","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.13458"}},"official":{"repos":["cbuelt/dduq"],"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"]}}},{"url":"/paper/understanding-why-label-smoothing-degrades","slug":"understanding-why-label-smoothing-degrades","title":"Towards Understanding Why Label Smoothing Degrades Selective Classification and How to Fix It","date":"2024-03-19","arxiv_id":"2403.14715","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":2,"n_ran_checked":8,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"9 ran (of which 2 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/understanding-why-label-smoothing-degrades#ran","syntology_url":"https://syntology.ai/paper/2403.14715","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.14715"}},"official":{"repos":["ENSTA-U2IS-AI/Label-smoothing-Selective-classification-Code"],"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":["found_in_text","official"]}}},{"url":"/paper/estimation-and-analysis-of-slice-propagation","slug":"estimation-and-analysis-of-slice-propagation","title":"Estimation and Analysis of Slice Propagation Uncertainty in 3D Anatomy Segmentation","date":"2024-03-18","arxiv_id":"2403.12290","repositories_listed":1,"syntology":null},{"url":"/paper/posterior-uncertainty-quantification-in","slug":"posterior-uncertainty-quantification-in","title":"Posterior Uncertainty Quantification in Neural Networks using Data Augmentation","date":"2024-03-18","arxiv_id":"2403.12729","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"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) · 0 unverified","sample_list":"/paper/posterior-uncertainty-quantification-in#ran","syntology_url":"https://syntology.ai/paper/2403.12729","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.12729"}},"official":{"repos":["apple/ml-mixupmp"],"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","unlocated"]}}},{"url":"/paper/function-space-parameterization-of-neural","slug":"function-space-parameterization-of-neural","title":"Function-space Parameterization of Neural Networks for Sequential Learning","date":"2024-03-16","arxiv_id":"2403.10929","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_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","sample_list":"/paper/function-space-parameterization-of-neural#ran","syntology_url":"https://syntology.ai/paper/2403.10929","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10929"}},"official":{"repos":["AaltoML/sfr-experiments"],"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"]}}},{"url":"/paper/using-uncertainty-quantification-to","slug":"using-uncertainty-quantification-to","title":"Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs","date":"2024-03-15","arxiv_id":"2403.10642","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_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","sample_list":"/paper/using-uncertainty-quantification-to#ran","syntology_url":"https://syntology.ai/paper/2403.10642","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10642"}},"official":{"repos":["amazon-science/operator-probconserv"],"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"]}}},{"url":"/paper/uncertainty-quantification-for-cross-subject","slug":"uncertainty-quantification-for-cross-subject","title":"Uncertainty Quantification for cross-subject Motor Imagery classification","date":"2024-03-14","arxiv_id":"2403.09228","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-convergence-of-locally-adaptive-and","slug":"on-the-convergence-of-locally-adaptive-and","title":"On the Convergence of Locally Adaptive and Scalable Diffusion-Based Sampling Methods for Deep Bayesian Neural Network Posteriors","date":"2024-03-13","arxiv_id":"2403.08609","repositories_listed":1,"syntology":null},{"url":"/paper/scalable-spatiotemporal-prediction-with","slug":"scalable-spatiotemporal-prediction-with","title":"Scalable Spatiotemporal Prediction with Bayesian Neural Fields","date":"2024-03-12","arxiv_id":"2403.07657","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_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","sample_list":"/paper/scalable-spatiotemporal-prediction-with#ran","syntology_url":"https://syntology.ai/paper/2403.07657","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.07657"}},"official":{"repos":["google/bayesnf"],"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"]}}},{"url":"/paper/koopman-ensembles-for-probabilistic-time","slug":"koopman-ensembles-for-probabilistic-time","title":"Koopman Ensembles for Probabilistic Time Series Forecasting","date":"2024-03-11","arxiv_id":"2403.06757","repositories_listed":1,"syntology":null},{"url":"/paper/fact-checking-the-output-of-large-language","slug":"fact-checking-the-output-of-large-language","title":"Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification","date":"2024-03-07","arxiv_id":"2403.04696","repositories_listed":1,"syntology":null},{"url":"/paper/confidence-on-the-focal-conformal-prediction","slug":"confidence-on-the-focal-conformal-prediction","title":"Confidence on the Focal: Conformal Prediction with Selection-Conditional Coverage","date":"2024-03-06","arxiv_id":"2403.03868","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"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","sample_list":"/paper/confidence-on-the-focal-conformal-prediction#ran","syntology_url":"https://syntology.ai/paper/2403.03868","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.03868"}},"official":{"repos":["ying531/jomi-paper"],"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"]}}},{"url":"/paper/conformal-prediction-for-multi-dimensional","slug":"conformal-prediction-for-multi-dimensional","title":"Conformal prediction for multi-dimensional time series by ellipsoidal sets","date":"2024-03-06","arxiv_id":"2403.03850","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_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","sample_list":"/paper/conformal-prediction-for-multi-dimensional#ran","syntology_url":"https://syntology.ai/paper/2403.03850","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.03850"}},"official":{"repos":["hamrel-cxu/multidimspci"],"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"]}}},{"url":"/paper/structured-methods-for-parameter-inference","slug":"structured-methods-for-parameter-inference","title":"Structured methods for parameter inference and uncertainty quantification for mechanistic models in the life sciences","date":"2024-03-04","arxiv_id":"2403.01678","repositories_listed":1,"syntology":null},{"url":"/paper/extracting-usable-predictions-from-quantized","slug":"extracting-usable-predictions-from-quantized","title":"Extracting Usable Predictions from Quantized Networks through Uncertainty Quantification for OOD Detection","date":"2024-03-02","arxiv_id":"2403.01076","repositories_listed":1,"syntology":null},{"url":"/paper/validation-of-ml-uq-calibration-statistics","slug":"validation-of-ml-uq-calibration-statistics","title":"Validation of ML-UQ calibration statistics using simulated reference values: a sensitivity analysis","date":"2024-03-01","arxiv_id":"2403.00423","repositories_listed":1,"syntology":null},{"url":"/paper/a-priori-uncertainty-quantification-of","slug":"a-priori-uncertainty-quantification-of","title":"A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural Networks","date":"2024-02-28","arxiv_id":"2402.18729","repositories_listed":1,"syntology":null},{"url":"/paper/outlier-detection-for-reactive-machine","slug":"outlier-detection-for-reactive-machine","title":"Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces","date":"2024-02-27","arxiv_id":"2402.17686","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-quantification-in-anomaly","slug":"uncertainty-quantification-in-anomaly","title":"Uncertainty Quantification in Anomaly Detection with Cross-Conformal $p$-Values","date":"2024-02-26","arxiv_id":"2402.16388","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-aware-evaluation-for-vision","slug":"uncertainty-aware-evaluation-for-vision","title":"Uncertainty-Aware Evaluation for Vision-Language Models","date":"2024-02-22","arxiv_id":"2402.14418","repositories_listed":1,"syntology":{"n":17,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":9,"n_honours":0,"n_violates":1,"n_no_contract":7,"n_pointer_only":0,"phrase":"8 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; 0 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/uncertainty-aware-evaluation-for-vision#ran","syntology_url":"https://syntology.ai/paper/2402.14418","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.14418"}},"official":{"repos":["ensec-ai/vlm-uncertainty-bench"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/accuracy-preserving-calibration-via","slug":"accuracy-preserving-calibration-via","title":"Accuracy-Preserving Calibration via Statistical Modeling on Probability Simplex","date":"2024-02-21","arxiv_id":"2402.13765","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-quantification-in-fine-tuned-llms","slug":"uncertainty-quantification-in-fine-tuned-llms","title":"Uncertainty quantification in fine-tuned LLMs using LoRA ensembles","date":"2024-02-19","arxiv_id":"2402.12264","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":5,"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) · 0 unverified","sample_list":"/paper/uncertainty-quantification-in-fine-tuned-llms#ran","syntology_url":"https://syntology.ai/paper/2402.12264","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.12264"}},"official":{"repos":["oleksandr-balabanov/equivariant-posteriors"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/training-bayesian-neural-networks-with-sparse","slug":"training-bayesian-neural-networks-with-sparse","title":"Training Bayesian Neural Networks with Sparse Subspace Variational Inference","date":"2024-02-16","arxiv_id":"2402.11025","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 1 unverified","sample_list":"/paper/training-bayesian-neural-networks-with-sparse#ran","syntology_url":"https://syntology.ai/paper/2402.11025","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.11025"}},"official":{"repos":["ljb121002/ssvi"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/extrapolation-aware-nonparametric-statistical","slug":"extrapolation-aware-nonparametric-statistical","title":"Extrapolation-Aware Nonparametric Statistical Inference","date":"2024-02-15","arxiv_id":"2402.09758","repositories_listed":1,"syntology":null},{"url":"/paper/how-to-validate-average-calibration-for","slug":"how-to-validate-average-calibration-for","title":"Negative impact of heavy-tailed uncertainty and error distributions on the reliability of calibration statistics for machine learning regression tasks","date":"2024-02-15","arxiv_id":"2402.10043","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-decomposition-and-quantification","slug":"uncertainty-decomposition-and-quantification","title":"Uncertainty Quantification for In-Context Learning of Large Language Models","date":"2024-02-15","arxiv_id":"2402.10189","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_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) · 3 unverified","sample_list":"/paper/uncertainty-decomposition-and-quantification#ran","syntology_url":"https://syntology.ai/paper/2402.10189","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.10189"}},"official":{"repos":["lingchen0331/uq_icl"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/improved-evidential-deep-learning-via-a","slug":"improved-evidential-deep-learning-via-a","title":"Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?","date":"2024-02-09","arxiv_id":"2402.06160","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"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","sample_list":"/paper/improved-evidential-deep-learning-via-a#ran","syntology_url":"https://syntology.ai/paper/2402.06160","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.06160"}},"official":{"repos":["maohaos2/edl-mirage"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/introspective-planning-guiding-language","slug":"introspective-planning-guiding-language","title":"Introspective Planning: Aligning Robots' Uncertainty with Inherent Task Ambiguity","date":"2024-02-09","arxiv_id":"2402.06529","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_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","sample_list":"/paper/introspective-planning-guiding-language#ran","syntology_url":"https://syntology.ai/paper/2402.06529","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.06529"}},"official":{"repos":["kevinliang888/IntroPlan"],"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"]}}},{"url":"/paper/classification-under-nuisance-parameters-and","slug":"classification-under-nuisance-parameters-and","title":"Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference","date":"2024-02-08","arxiv_id":"2402.05330","repositories_listed":1,"syntology":null}],"record_sha256":"08261ac0d40f545f5820134b6b324125c19965b54c67ea9ecfc74c00abb0403f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}