{"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/2","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":2,"pages_in_order":24,"rows_per_page":100,"rows":[101,200],"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","next":"/task/uncertainty-quantification/papers/3","papers":[{"url":"/paper/uqlm-a-python-package-for-uncertainty","slug":"uqlm-a-python-package-for-uncertainty","title":"UQLM: A Python Package for Uncertainty Quantification in Large Language Models","date":"2025-07-08","arxiv_id":"2507.06196","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/uqlm-a-python-package-for-uncertainty#ran","syntology_url":"https://syntology.ai/paper/2507.06196","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2507.06196"}},"official":{"repos":["cvs-health/uqlm"],"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/scalable-bayesian-low-rank-adaptation-of","slug":"scalable-bayesian-low-rank-adaptation-of","title":"Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference","date":"2025-06-26","arxiv_id":"2506.21408","repositories_listed":1,"syntology":null},{"url":"/paper/consensus-driven-uncertainty-for-robotic","slug":"consensus-driven-uncertainty-for-robotic","title":"Consensus-Driven Uncertainty for Robotic Grasping based on RGB Perception","date":"2025-06-24","arxiv_id":"2506.20045","repositories_listed":1,"syntology":null},{"url":"/paper/gnn-s-uncertainty-quantification-using-self","slug":"gnn-s-uncertainty-quantification-using-self","title":"GNN's Uncertainty Quantification using Self-Distillation","date":"2025-06-24","arxiv_id":"2506.20046","repositories_listed":1,"syntology":null},{"url":"/paper/uprop-investigating-the-uncertainty","slug":"uprop-investigating-the-uncertainty","title":"UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making","date":"2025-06-20","arxiv_id":"2506.17419","repositories_listed":1,"syntology":null},{"url":"/paper/vine-copulas-as-differentiable-computational","slug":"vine-copulas-as-differentiable-computational","title":"Vine Copulas as Differentiable Computational Graphs","date":"2025-06-16","arxiv_id":"2506.13318","repositories_listed":1,"syntology":null},{"url":"/paper/recursive-kalmannet-deep-learning-augmented","slug":"recursive-kalmannet-deep-learning-augmented","title":"Recursive KalmanNet: Deep Learning-Augmented Kalman Filtering for State Estimation with Consistent Uncertainty Quantification","date":"2025-06-13","arxiv_id":"2506.11639","repositories_listed":1,"syntology":null},{"url":"/paper/statistical-machine-learning-for-astronomy-a","slug":"statistical-machine-learning-for-astronomy-a","title":"Statistical Machine Learning for Astronomy -- A Textbook","date":"2025-06-13","arxiv_id":"2506.12230","repositories_listed":1,"syntology":null},{"url":"/paper/improving-group-robustness-on-spurious-1","slug":"improving-group-robustness-on-spurious-1","title":"Improving Group Robustness on Spurious Correlation via Evidential Alignment","date":"2025-06-12","arxiv_id":"2506.11347","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_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","sample_list":"/paper/improving-group-robustness-on-spurious-1#ran","syntology_url":"https://syntology.ai/paper/2506.11347","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.11347"}},"official":{"repos":["wenqian-ye/evidential_alignment"],"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"]}}},{"url":"/paper/inv-entropy-a-fully-probabilistic-framework","slug":"inv-entropy-a-fully-probabilistic-framework","title":"Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language Models","date":"2025-06-11","arxiv_id":"2506.09684","repositories_listed":1,"syntology":null},{"url":"/paper/ladcast-a-latent-diffusion-model-for-medium","slug":"ladcast-a-latent-diffusion-model-for-medium","title":"LaDCast: A Latent Diffusion Model for Medium-Range Ensemble Weather Forecasting","date":"2025-06-10","arxiv_id":"2506.09193","repositories_listed":1,"syntology":null},{"url":"/paper/emulating-compact-binary-population-synthesis","slug":"emulating-compact-binary-population-synthesis","title":"Emulating compact binary population synthesis simulations with robust uncertainty quantification and model comparison: Bayesian normalizing flows","date":"2025-06-06","arxiv_id":"2506.05657","repositories_listed":1,"syntology":null},{"url":"/paper/testing-hypotheses-of-covariate-effects-on","slug":"testing-hypotheses-of-covariate-effects-on","title":"Testing Hypotheses of Covariate Effects on Topics of Discourse","date":"2025-06-05","arxiv_id":"2506.05570","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-word-level-quality-estimation","slug":"unsupervised-word-level-quality-estimation","title":"Unsupervised Word-level Quality Estimation for Machine Translation Through the Lens of Annotators (Dis)agreement","date":"2025-05-29","arxiv_id":"2505.23183","repositories_listed":1,"syntology":null},{"url":"/paper/optimal-conformal-prediction-under-epistemic","slug":"optimal-conformal-prediction-under-epistemic","title":"Optimal Conformal Prediction under Epistemic Uncertainty","date":"2025-05-25","arxiv_id":"2505.19033","repositories_listed":1,"syntology":null},{"url":"/paper/a-generic-framework-for-conformal-fairness","slug":"a-generic-framework-for-conformal-fairness","title":"A Generic Framework for Conformal Fairness","date":"2025-05-22","arxiv_id":"2505.16115","repositories_listed":1,"syntology":{"n":19,"n_ran":15,"n_constructed":14,"n_ran_checked":14,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":0,"phrase":"15 ran (of which 14 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/a-generic-framework-for-conformal-fairness#ran","syntology_url":"https://syntology.ai/paper/2505.16115","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.16115"}},"official":{"repos":["adityavadlamani/conformal-fairness"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":14,"n_ran_no_instrument_failure":14,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/uncertainty-quantification-in-svm-prediction","slug":"uncertainty-quantification-in-svm-prediction","title":"Uncertainty Quantification in SVM prediction","date":"2025-05-21","arxiv_id":"2505.15429","repositories_listed":1,"syntology":null},{"url":"/paper/survunc-a-meta-model-based-uncertainty","slug":"survunc-a-meta-model-based-uncertainty","title":"SurvUnc: A Meta-Model Based Uncertainty Quantification Framework for Survival Analysis","date":"2025-05-20","arxiv_id":"2505.14803","repositories_listed":1,"syntology":null},{"url":"/paper/neurosymbolic-diffusion-models","slug":"neurosymbolic-diffusion-models","title":"Neurosymbolic Diffusion Models","date":"2025-05-19","arxiv_id":"2505.13138","repositories_listed":1,"syntology":null},{"url":"/paper/integrative-analysis-and-imputation-of","slug":"integrative-analysis-and-imputation-of","title":"Integrative Analysis and Imputation of Multiple Data Streams via Deep Gaussian Processes","date":"2025-05-17","arxiv_id":"2505.12076","repositories_listed":1,"syntology":null},{"url":"/paper/modeles-de-substitution-pour-les-modeles-a","slug":"modeles-de-substitution-pour-les-modeles-a","title":"Modèles de Substitution pour les Modèles à base d'Agents : Enjeux, Méthodes et Applications","date":"2025-05-17","arxiv_id":"2505.11912","repositories_listed":1,"syntology":null},{"url":"/paper/2505-11190","slug":"2505-11190","title":"JaxSGMC: Modular stochastic gradient MCMC in JAX","date":"2025-05-16","arxiv_id":"2505.11190","repositories_listed":1,"syntology":null},{"url":"/paper/stable-and-convexified-information-bottleneck","slug":"stable-and-convexified-information-bottleneck","title":"Stable and Convexified Information Bottleneck Optimization via Symbolic Continuation and Entropy-Regularized Trajectories","date":"2025-05-14","arxiv_id":"2505.09239","repositories_listed":1,"syntology":null},{"url":"/paper/a-head-to-predict-and-a-head-to-question-pre","slug":"a-head-to-predict-and-a-head-to-question-pre","title":"A Head to Predict and a Head to Question: Pre-trained Uncertainty Quantification Heads for Hallucination Detection in LLM Outputs","date":"2025-05-13","arxiv_id":"2505.08200","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 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; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/a-head-to-predict-and-a-head-to-question-pre#ran","syntology_url":"https://syntology.ai/paper/2505.08200","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.08200"}},"official":null}},{"url":"/paper/improved-uncertainty-quantification-in","slug":"improved-uncertainty-quantification-in","title":"Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles","date":"2025-05-09","arxiv_id":"2505.06459","repositories_listed":1,"syntology":null},{"url":"/paper/hibayes-a-hierarchical-bayesian-modeling","slug":"hibayes-a-hierarchical-bayesian-modeling","title":"HiBayES: A Hierarchical Bayesian Modeling Framework for AI Evaluation Statistics","date":"2025-05-08","arxiv_id":"2505.05602","repositories_listed":1,"syntology":null},{"url":"/paper/probabilistic-embeddings-for-frozen-vision","slug":"probabilistic-embeddings-for-frozen-vision","title":"Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models","date":"2025-05-08","arxiv_id":"2505.05163","repositories_listed":1,"syntology":null},{"url":"/paper/regression-based-melody-estimation-with","slug":"regression-based-melody-estimation-with","title":"Regression-based Melody Estimation with Uncertainty Quantification","date":"2025-05-08","arxiv_id":"2505.05156","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainsam-fast-and-efficient-uncertainty","slug":"uncertainsam-fast-and-efficient-uncertainty","title":"UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model","date":"2025-05-08","arxiv_id":"2505.05049","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/uncertainsam-fast-and-efficient-uncertainty#ran","syntology_url":"https://syntology.ai/paper/2505.05049","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.05049"}},"official":{"repos":["GreenAutoML4FAS/UncertainSAM"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/conformal-prediction-with-corrupted-labels","slug":"conformal-prediction-with-corrupted-labels","title":"Conformal Prediction with Corrupted Labels: Uncertain Imputation and Robust Re-weighting","date":"2025-05-07","arxiv_id":"2505.04733","repositories_listed":1,"syntology":null},{"url":"/paper/full-field-surrogate-modeling-of-cardiac","slug":"full-field-surrogate-modeling-of-cardiac","title":"Full-field surrogate modeling of cardiac function encoding geometric variability","date":"2025-04-29","arxiv_id":"2504.20479","repositories_listed":1,"syntology":null},{"url":"/paper/gauss-mi-gaussian-splatting-shannon-mutual","slug":"gauss-mi-gaussian-splatting-shannon-mutual","title":"GauSS-MI: Gaussian Splatting Shannon Mutual Information for Active 3D Reconstruction","date":"2025-04-29","arxiv_id":"2504.21067","repositories_listed":1,"syntology":null},{"url":"/paper/model-uncertainty-quantification-using","slug":"model-uncertainty-quantification-using","title":"Model uncertainty quantification using feature confidence sets for outcome excursions","date":"2025-04-28","arxiv_id":"2504.19464","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-quantification-for-language","slug":"uncertainty-quantification-for-language","title":"Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorers","date":"2025-04-27","arxiv_id":"2504.19254","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-uncertainty-in-deep-gaussian","slug":"evaluating-uncertainty-in-deep-gaussian","title":"Evaluating Uncertainty in Deep Gaussian Processes","date":"2025-04-24","arxiv_id":"2504.17719","repositories_listed":1,"syntology":null},{"url":"/paper/achieving-distributive-justice-in-federated","slug":"achieving-distributive-justice-in-federated","title":"Achieving Distributive Justice in Federated Learning via Uncertainty Quantification","date":"2025-04-22","arxiv_id":"2504.15924","repositories_listed":1,"syntology":null},{"url":"/paper/learning-enhanced-structural-representations","slug":"learning-enhanced-structural-representations","title":"Learning Enhanced Structural Representations with Block-Based Uncertainties for Ocean Floor Mapping","date":"2025-04-19","arxiv_id":"2504.14372","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-the-potential-for-large-language","slug":"exploring-the-potential-for-large-language","title":"Exploring the Potential for Large Language Models to Demonstrate Rational Probabilistic Beliefs","date":"2025-04-18","arxiv_id":"2504.13644","repositories_listed":1,"syntology":null},{"url":"/paper/leave-one-out-stable-conformal-prediction","slug":"leave-one-out-stable-conformal-prediction","title":"Leave-One-Out Stable Conformal Prediction","date":"2025-04-16","arxiv_id":"2504.12189","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"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) · 1 unverified","sample_list":"/paper/leave-one-out-stable-conformal-prediction#ran","syntology_url":"https://syntology.ai/paper/2504.12189","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.12189"}},"official":{"repos":["KiljaeL/LOO-StabCP"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/adaptive-shrinkage-estimation-for","slug":"adaptive-shrinkage-estimation-for","title":"Adaptive Shrinkage Estimation For Personalized Deep Kernel Regression In Modeling Brain Trajectories","date":"2025-04-10","arxiv_id":"2504.08840","repositories_listed":1,"syntology":null},{"url":"/paper/adapting-gt2-fls-for-uncertainty","slug":"adapting-gt2-fls-for-uncertainty","title":"Adapting GT2-FLS for Uncertainty Quantification: A Blueprint Calibration Strategy","date":"2025-04-09","arxiv_id":"2504.07017","repositories_listed":1,"syntology":null},{"url":"/paper/point-2-a-polymer-informatics-training-and","slug":"point-2-a-polymer-informatics-training-and","title":"POINT$^{2}$: A Polymer Informatics Training and Testing Database","date":"2025-03-30","arxiv_id":"2503.23491","repositories_listed":1,"syntology":null},{"url":"/paper/equino-a-physics-informed-neural-operator-for","slug":"equino-a-physics-informed-neural-operator-for","title":"EquiNO: A Physics-Informed Neural Operator for Multiscale Simulations","date":"2025-03-27","arxiv_id":"2504.07976","repositories_listed":1,"syntology":null},{"url":"/paper/llm-based-agent-simulation-for-maternal","slug":"llm-based-agent-simulation-for-maternal","title":"LLM-based Agent Simulation for Maternal Health Interventions: Uncertainty Estimation and Decision-focused Evaluation","date":"2025-03-25","arxiv_id":"2503.22719","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"5 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; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/llm-based-agent-simulation-for-maternal#ran","syntology_url":"https://syntology.ai/paper/2503.22719","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.22719"}},"official":{"repos":["sarahmart/llm-abs-armman-prediction"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/language-model-uncertainty-quantification","slug":"language-model-uncertainty-quantification","title":"Language Model Uncertainty Quantification with Attention Chain","date":"2025-03-24","arxiv_id":"2503.19168","repositories_listed":1,"syntology":null},{"url":"/paper/neuralfoil-an-airfoil-aerodynamics-analysis","slug":"neuralfoil-an-airfoil-aerodynamics-analysis","title":"NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning","date":"2025-03-20","arxiv_id":"2503.16323","repositories_listed":1,"syntology":null},{"url":"/paper/neurosep-cp-lcb-a-deep-learning-based","slug":"neurosep-cp-lcb-a-deep-learning-based","title":"NeuroSep-CP-LCB: A Deep Learning-based Contextual Multi-armed Bandit Algorithm with Uncertainty Quantification for Early Sepsis Prediction","date":"2025-03-20","arxiv_id":"2503.16708","repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-modeling-of-zero-shot","slug":"bayesian-modeling-of-zero-shot","title":"Bayesian Modeling of Zero-Shot Classifications for Urban Flood Detection","date":"2025-03-18","arxiv_id":"2503.14754","repositories_listed":1,"syntology":null},{"url":"/paper/on-traffic-an-operator-learning-framework-for","slug":"on-traffic-an-operator-learning-framework-for","title":"ON-Traffic: An Operator Learning Framework for Online Traffic Flow Estimation and Uncertainty Quantification from Lagrangian Sensors","date":"2025-03-18","arxiv_id":"2503.14053","repositories_listed":1,"syntology":null},{"url":"/paper/reliable-uncertainty-quantification-for-2d-3d","slug":"reliable-uncertainty-quantification-for-2d-3d","title":"Reliable uncertainty quantification for 2D/3D anatomical landmark localization using multi-output conformal prediction","date":"2025-03-18","arxiv_id":"2503.14106","repositories_listed":1,"syntology":null},{"url":"/paper/sepsyn-olcp-an-online-learning-based","slug":"sepsyn-olcp-an-online-learning-based","title":"Sepsyn-OLCP: An Online Learning-based Framework for Early Sepsis Prediction with Uncertainty Quantification using Conformal Prediction","date":"2025-03-18","arxiv_id":"2503.14663","repositories_listed":1,"syntology":null},{"url":"/paper/e-values-expand-the-scope-of-conformal","slug":"e-values-expand-the-scope-of-conformal","title":"E-Values Expand the Scope of Conformal Prediction","date":"2025-03-17","arxiv_id":"2503.13050","repositories_listed":1,"syntology":null},{"url":"/paper/on-local-posterior-structure-in-deep","slug":"on-local-posterior-structure-in-deep","title":"On Local Posterior Structure in Deep Ensembles","date":"2025-03-17","arxiv_id":"2503.13296","repositories_listed":1,"syntology":null},{"url":"/paper/survival-analysis-with-machine-learning-for","slug":"survival-analysis-with-machine-learning-for","title":"Survival Analysis with Machine Learning for Predicting Li-ion Battery Remaining Useful Life","date":"2025-03-17","arxiv_id":"2503.13558","repositories_listed":1,"syntology":null},{"url":"/paper/the-architecture-and-evaluation-of-bayesian","slug":"the-architecture-and-evaluation-of-bayesian","title":"Understanding the Trade-offs in Accuracy and Uncertainty Quantification: Architecture and Inference Choices in Bayesian Neural Networks","date":"2025-03-14","arxiv_id":"2503.11808","repositories_listed":1,"syntology":null},{"url":"/paper/mamba-time-series-forecasting-with","slug":"mamba-time-series-forecasting-with","title":"Mamba time series forecasting with uncertainty quantification","date":"2025-03-13","arxiv_id":"2503.10873","repositories_listed":1,"syntology":null},{"url":"/paper/occuq-exploring-efficient-uncertainty","slug":"occuq-exploring-efficient-uncertainty","title":"OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy Prediction","date":"2025-03-13","arxiv_id":"2503.10605","repositories_listed":1,"syntology":null},{"url":"/paper/can-we-detect-failures-without-failure-data","slug":"can-we-detect-failures-without-failure-data","title":"Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning Policies","date":"2025-03-11","arxiv_id":"2503.08558","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-quantification-and-posterior","slug":"uncertainty-quantification-and-posterior","title":"Uncertainty quantification and posterior sampling for network reconstruction","date":"2025-03-10","arxiv_id":"2503.07736","repositories_listed":1,"syntology":null},{"url":"/paper/learning-and-discovering-multiple-solutions","slug":"learning-and-discovering-multiple-solutions","title":"Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble","date":"2025-03-08","arxiv_id":"2503.06320","repositories_listed":1,"syntology":null},{"url":"/paper/conformal-prediction-for-image-segmentation","slug":"conformal-prediction-for-image-segmentation","title":"Conformal Prediction for Image Segmentation Using Morphological Prediction Sets","date":"2025-03-07","arxiv_id":"2503.05618","repositories_listed":1,"syntology":null},{"url":"/paper/conceptualizing-uncertainty","slug":"conceptualizing-uncertainty","title":"Conceptualizing Uncertainty","date":"2025-03-05","arxiv_id":"2503.03443","repositories_listed":1,"syntology":null},{"url":"/paper/can-diffusion-models-provide-rigorous","slug":"can-diffusion-models-provide-rigorous","title":"Can Diffusion Models Provide Rigorous Uncertainty Quantification for Bayesian Inverse Problems?","date":"2025-03-04","arxiv_id":"2503.03007","repositories_listed":1,"syntology":null},{"url":"/paper/architectural-and-inferential-inductive","slug":"architectural-and-inferential-inductive","title":"Architectural and Inferential Inductive Biases For Exchangeable Sequence Modeling","date":"2025-03-03","arxiv_id":"2503.01215","repositories_listed":1,"syntology":null},{"url":"/paper/an-ensemble-framework-for-probabilistic-short","slug":"an-ensemble-framework-for-probabilistic-short","title":"An Ensemble Framework for Probabilistic Short-Term Load Forecasting Based on BiTCN and Deep Attention Networks","date":"2025-02-25","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-quantification-in-retrieval","slug":"uncertainty-quantification-in-retrieval","title":"Uncertainty Quantification in Retrieval Augmented Question Answering","date":"2025-02-25","arxiv_id":"2502.18108","repositories_listed":1,"syntology":null},{"url":"/paper/cot-uq-improving-response-wise-uncertainty","slug":"cot-uq-improving-response-wise-uncertainty","title":"CoT-UQ: Improving Response-wise Uncertainty Quantification in LLMs with Chain-of-Thought","date":"2025-02-24","arxiv_id":"2502.17214","repositories_listed":1,"syntology":null},{"url":"/paper/posterior-inference-with-diffusion-models-for","slug":"posterior-inference-with-diffusion-models-for","title":"Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization","date":"2025-02-24","arxiv_id":"2502.16824","repositories_listed":1,"syntology":null},{"url":"/paper/forecasting-local-ionospheric-parameters","slug":"forecasting-local-ionospheric-parameters","title":"Forecasting Local Ionospheric Parameters Using Transformers","date":"2025-02-20","arxiv_id":"2502.15093","repositories_listed":1,"syntology":null},{"url":"/paper/sparse-activations-as-conformal-predictors","slug":"sparse-activations-as-conformal-predictors","title":"Sparse Activations as Conformal Predictors","date":"2025-02-20","arxiv_id":"2502.14773","repositories_listed":1,"syntology":null},{"url":"/paper/token-level-density-based-uncertainty","slug":"token-level-density-based-uncertainty","title":"Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models","date":"2025-02-20","arxiv_id":"2502.14427","repositories_listed":1,"syntology":null},{"url":"/paper/probabilistic-neural-operators-for-functional","slug":"probabilistic-neural-operators-for-functional","title":"Probabilistic neural operators for functional uncertainty quantification","date":"2025-02-18","arxiv_id":"2502.12902","repositories_listed":1,"syntology":null},{"url":"/paper/minimal-ranks-maximum-confidence-parameter","slug":"minimal-ranks-maximum-confidence-parameter","title":"Minimal Ranks, Maximum Confidence: Parameter-efficient Uncertainty Quantification for LoRA","date":"2025-02-17","arxiv_id":"2502.12122","repositories_listed":1,"syntology":null},{"url":"/paper/epidemic-guided-deep-learning-for","slug":"epidemic-guided-deep-learning-for","title":"Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreak","date":"2025-02-15","arxiv_id":"2502.10786","repositories_listed":1,"syntology":null},{"url":"/paper/reduced-order-modeling-with-shallow-recurrent","slug":"reduced-order-modeling-with-shallow-recurrent","title":"Reduced Order Modeling with Shallow Recurrent Decoder Networks","date":"2025-02-15","arxiv_id":"2502.10930","repositories_listed":1,"syntology":null},{"url":"/paper/adapts-adapting-univariate-foundation-models","slug":"adapts-adapting-univariate-foundation-models","title":"AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting","date":"2025-02-14","arxiv_id":"2502.10235","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"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) · 3 unverified","sample_list":"/paper/adapts-adapting-univariate-foundation-models#ran","syntology_url":"https://syntology.ai/paper/2502.10235","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.10235"}},"official":{"repos":["abenechehab/adapts"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/inverse-design-with-dynamic-mode","slug":"inverse-design-with-dynamic-mode","title":"Inverse Design with Dynamic Mode Decomposition","date":"2025-02-13","arxiv_id":"2502.09490","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-confidence-adaptive-abstention-in-dual","slug":"beyond-confidence-adaptive-abstention-in-dual","title":"Beyond Confidence: Adaptive Abstention in Dual-Threshold Conformal Prediction for Autonomous System Perception","date":"2025-02-11","arxiv_id":"2502.07255","repositories_listed":1,"syntology":null},{"url":"/paper/epistemic-uncertainty-in-conformal-scores-a","slug":"epistemic-uncertainty-in-conformal-scores-a","title":"Epistemic Uncertainty in Conformal Scores: A Unified Approach","date":"2025-02-10","arxiv_id":"2502.06995","repositories_listed":1,"syntology":null},{"url":"/paper/microcanonical-langevin-ensembles-advancing","slug":"microcanonical-langevin-ensembles-advancing","title":"Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks","date":"2025-02-10","arxiv_id":"2502.06335","repositories_listed":1,"syntology":null},{"url":"/paper/lipschitz-driven-inference-bias-corrected","slug":"lipschitz-driven-inference-bias-corrected","title":"Smooth Sailing: Lipschitz-Driven Uncertainty Quantification for Spatial Association","date":"2025-02-09","arxiv_id":"2502.06067","repositories_listed":1,"syntology":null},{"url":"/paper/learning-conformal-abstention-policies-for","slug":"learning-conformal-abstention-policies-for","title":"Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language Models","date":"2025-02-08","arxiv_id":"2502.06884","repositories_listed":1,"syntology":{"n":16,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":8,"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, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/learning-conformal-abstention-policies-for#ran","syntology_url":"https://syntology.ai/paper/2502.06884","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.06884"}},"official":{"repos":["sinatayebati/vlm-uncertainty"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/capturing-extreme-events-in-turbulence-using","slug":"capturing-extreme-events-in-turbulence-using","title":"Capturing Extreme Events in Turbulence using an Extreme Variational Autoencoder (xVAE)","date":"2025-02-07","arxiv_id":"2502.04685","repositories_listed":1,"syntology":null},{"url":"/paper/cocoa-a-generalized-approach-to-uncertainty","slug":"cocoa-a-generalized-approach-to-uncertainty","title":"Uncertainty Quantification for LLMs through Minimum Bayes Risk: Bridging Confidence and Consistency","date":"2025-02-07","arxiv_id":"2502.04964","repositories_listed":1,"syntology":null},{"url":"/paper/temporal-distribution-shift-in-real-world","slug":"temporal-distribution-shift-in-real-world","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","date":"2025-02-06","arxiv_id":"2502.03982","repositories_listed":1,"syntology":null},{"url":"/paper/parametric-scaling-law-of-tuning-bias-in","slug":"parametric-scaling-law-of-tuning-bias-in","title":"Parametric Scaling Law of Tuning Bias in Conformal Prediction","date":"2025-02-05","arxiv_id":"2502.03023","repositories_listed":1,"syntology":null},{"url":"/paper/from-uncertain-to-safe-conformal-fine-tuning","slug":"from-uncertain-to-safe-conformal-fine-tuning","title":"From Uncertain to Safe: Conformal Fine-Tuning of Diffusion Models for Safe PDE Control","date":"2025-02-04","arxiv_id":"2502.02205","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":2,"n_no_contract":3,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 2 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/from-uncertain-to-safe-conformal-fine-tuning#ran","syntology_url":"https://syntology.ai/paper/2502.02205","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.02205"}},"official":{"repos":["ai4science-westlakeu/safediffcon"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/optimal-subspace-inference-for-the-laplace","slug":"optimal-subspace-inference-for-the-laplace","title":"Optimal Subspace Inference for the Laplace Approximation of Bayesian Neural Networks","date":"2025-02-04","arxiv_id":"2502.02345","repositories_listed":1,"syntology":null},{"url":"/paper/robust-and-conjugate-spatio-temporal-gaussian","slug":"robust-and-conjugate-spatio-temporal-gaussian","title":"Robust and Conjugate Spatio-Temporal Gaussian Processes","date":"2025-02-04","arxiv_id":"2502.02450","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/robust-and-conjugate-spatio-temporal-gaussian#ran","syntology_url":"https://syntology.ai/paper/2502.02450","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.02450"}},"official":{"repos":["williamlaplante/st-rcgp"],"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/uasthn-uncertainty-aware-deep-homography","slug":"uasthn-uncertainty-aware-deep-homography","title":"UASTHN: Uncertainty-Aware Deep Homography Estimation for UAV Satellite-Thermal Geo-localization","date":"2025-02-03","arxiv_id":"2502.01035","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"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, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/uasthn-uncertainty-aware-deep-homography#ran","syntology_url":"https://syntology.ai/paper/2502.01035","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.01035"}},"official":{"repos":["arplaboratory/UASTHN"],"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"]}}},{"url":"/paper/error-quantified-conformal-inference-for-time","slug":"error-quantified-conformal-inference-for-time","title":"Error-quantified Conformal Inference for Time Series","date":"2025-02-02","arxiv_id":"2502.00818","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/error-quantified-conformal-inference-for-time#ran","syntology_url":"https://syntology.ai/paper/2502.00818","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.00818"}},"official":{"repos":["creator-xi/Error-quantified-Conformal-Inference"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/pde-dkl-pde-constrained-deep-kernel-learning","slug":"pde-dkl-pde-constrained-deep-kernel-learning","title":"PDE-DKL: PDE-constrained deep kernel learning in high dimensionality","date":"2025-01-30","arxiv_id":"2501.18258","repositories_listed":1,"syntology":null},{"url":"/paper/reducing-aleatoric-and-epistemic-uncertainty","slug":"reducing-aleatoric-and-epistemic-uncertainty","title":"Reducing Aleatoric and Epistemic Uncertainty through Multi-modal Data Acquisition","date":"2025-01-30","arxiv_id":"2501.18268","repositories_listed":1,"syntology":null},{"url":"/paper/noise-adaptive-conformal-classification-with","slug":"noise-adaptive-conformal-classification-with","title":"Noise-Adaptive Conformal Classification with Marginal Coverage","date":"2025-01-29","arxiv_id":"2501.18060","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-quantification-and-decomposition","slug":"uncertainty-quantification-and-decomposition","title":"Uncertainty Quantification and Decomposition for LLM-based Recommendation","date":"2025-01-29","arxiv_id":"2501.17630","repositories_listed":1,"syntology":null},{"url":"/paper/approximate-message-passing-for-bayesian","slug":"approximate-message-passing-for-bayesian","title":"Approximate Message Passing for Bayesian Neural Networks","date":"2025-01-26","arxiv_id":"2501.15573","repositories_listed":1,"syntology":null},{"url":"/paper/visualizing-uncertainty-in-translation-tasks","slug":"visualizing-uncertainty-in-translation-tasks","title":"Visualizing Uncertainty in Translation Tasks: An Evaluation of LLM Performance and Confidence Metrics","date":"2025-01-26","arxiv_id":"2501.17187","repositories_listed":1,"syntology":null},{"url":"/paper/predictive-modeling-and-uncertainty","slug":"predictive-modeling-and-uncertainty","title":"Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning","date":"2025-01-25","arxiv_id":"2501.15057","repositories_listed":1,"syntology":null},{"url":"/paper/coverforest-conformal-predictions-with-random","slug":"coverforest-conformal-predictions-with-random","title":"coverforest: Conformal Predictions with Random Forest in Python","date":"2025-01-24","arxiv_id":"2501.14570","repositories_listed":1,"syntology":null},{"url":"/paper/making-reliable-and-flexible-decisions-in","slug":"making-reliable-and-flexible-decisions-in","title":"Making Reliable and Flexible Decisions in Long-tailed Classification","date":"2025-01-23","arxiv_id":"2501.14090","repositories_listed":1,"syntology":null}],"record_sha256":"d1eb953db8b1111d39a58dd09444822e597a02d35f69bd49fa10f2ad13b24274","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}