{"total":177,"rows":[{"id":"2306.13063","title":"Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs","created":"2023-06-22","cited_by_count":974,"area":null},{"id":"2107.03342","title":"A Survey of Uncertainty in Deep Neural Networks","created":"2021-07-07","cited_by_count":680,"area":null},{"id":"2109.14285","title":"Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration","created":"2021-09-29","cited_by_count":92,"area":null},{"id":"2111.15430","title":"The Devil is in the Margin: Margin-based Label Smoothing for Network Calibration","created":"2021-11-30","cited_by_count":91,"area":null},{"id":"2305.14535","title":"Uncertainty Quantification over Graph with Conformalized Graph Neural Networks","created":"2023-05-23","cited_by_count":79,"area":null},{"id":"2308.01222","title":"Calibration in Deep Learning: A Survey of the State-of-the-Art","created":"2023-08-02","cited_by_count":75,"area":null},{"id":"2112.10327","title":"Classifier Calibration: A survey on how to assess and improve predicted class probabilities","created":"2021-12-20","cited_by_count":73,"area":null},{"id":"2203.07835","title":"Better Uncertainty Calibration via Proper Scores for Classification and Beyond","created":"2022-03-15","cited_by_count":65,"area":null},{"id":"2211.16886","title":"A Unifying Theory of Distance from Calibration","created":"2022-11-30","cited_by_count":53,"area":null},{"id":"2102.12182","title":"Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration","created":"2021-02-24","cited_by_count":48,"area":null},{"id":"2210.07810","title":"A Consistent and Differentiable Lp Canonical Calibration Error Estimator","created":"2022-10-13","cited_by_count":47,"area":null},{"id":"2107.08353","title":"Top-label calibration and multiclass-to-binary reductions","created":"2021-07-18","cited_by_count":46,"area":null},{"id":"2310.19208","title":"LitCab: Lightweight Language Model Calibration over Short- and Long-form Responses","created":"2023-10-30","cited_by_count":44,"area":null},{"id":"2109.14591","title":"Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration","created":"2021-09-29","cited_by_count":40,"area":null},{"id":"2201.13291","title":"Metrics for saliency map evaluation of deep learning explanation methods","created":"2022-01-31","cited_by_count":40,"area":null},{"id":"2210.06391","title":"What Makes Graph Neural Networks Miscalibrated?","created":"2022-10-12","cited_by_count":40,"area":null},{"id":"2102.07856","title":"Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification","created":"2021-02-15","cited_by_count":39,"area":null},{"id":"2508.18847","title":"ConfTuner: Training Large Language Models to Express Their Confidence Verbally","created":"2025-08-26","cited_by_count":39,"area":null},{"id":"2105.04290","title":"Meta-Cal: Well-controlled Post-hoc Calibration by Ranking","created":"2021-05-10","cited_by_count":33,"area":null},{"id":"2305.13665","title":"Dual Focal Loss for Calibration","created":"2023-05-23","cited_by_count":33,"area":null},{"id":"2103.06132","title":"MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks","created":"2021-03-10","cited_by_count":32,"area":null},{"id":"2211.11838","title":"AdaFocal: Calibration-aware Adaptive Focal Loss","created":"2022-11-21","cited_by_count":32,"area":null},{"id":"2212.10888","title":"A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability","created":"2022-12-21","cited_by_count":31,"area":null},{"id":"2104.00742","title":"Confidence Calibration for Domain Generalization under Covariate Shift","created":"2021-04-01","cited_by_count":27,"area":null},{"id":"2402.11406","title":"Don't Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection","created":"2024-02-18","cited_by_count":25,"area":null},{"id":"2102.10809","title":"Local Calibration: Metrics and Recalibration","created":"2021-02-22","cited_by_count":24,"area":null},{"id":"2203.01850","title":"T-Cal: An optimal test for the calibration of predictive models","created":"2022-03-03","cited_by_count":24,"area":null},{"id":"2208.00461","title":"Adaptive Temperature Scaling for Robust Calibration of Deep Neural Networks","created":"2022-07-31","cited_by_count":24,"area":null},{"id":"2207.06873","title":"BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks","created":"2022-07-14","cited_by_count":23,"area":null},{"id":"2403.11491","title":"Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting","created":"2024-03-18","cited_by_count":20,"area":null},{"id":"2501.19047","title":"Understanding Model Calibration -- A gentle introduction and visual exploration of calibration and the expected calibration error (ECE)","created":"2025-01-31","cited_by_count":20,"area":null},{"id":"2503.02863","title":"SteerConf: Steering LLMs for Confidence Elicitation","created":"2025-03-04","cited_by_count":20,"area":null},{"id":"2306.04590","title":"Proximity-Informed Calibration for Deep Neural Networks","created":"2023-06-07","cited_by_count":18,"area":null},{"id":"2304.03646","title":"Fairness through Aleatoric Uncertainty","created":"2023-04-07","cited_by_count":16,"area":null},{"id":"2305.15508","title":"How to Fix a Broken Confidence Estimator: Evaluating Post-hoc Methods for Selective Classification with Deep Neural Networks","created":"2023-05-24","cited_by_count":16,"area":null},{"id":"2308.11911","title":"ACLS: Adaptive and Conditional Label Smoothing for Network Calibration","created":"2023-08-23","cited_by_count":15,"area":null},{"id":"2111.10734","title":"Deep Probability Estimation","created":"2021-11-21","cited_by_count":14,"area":null},{"id":"2203.07322","title":"Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation","created":"2022-03-14","cited_by_count":14,"area":null},{"id":"2302.05118","title":"Beyond In-Domain Scenarios: Robust Density-Aware Calibration","created":"2023-02-10","cited_by_count":14,"area":null},{"id":"2506.09669","title":"Query-Level Uncertainty in Large Language Models","created":"2025-06-11","cited_by_count":14,"area":null},{"id":"2202.07550","title":"Label fusion and training methods for reliable representation of inter-rater uncertainty","created":"2022-02-15","cited_by_count":13,"area":null},{"id":"2303.06268","title":"Trust your neighbours: Penalty-based constraints for model calibration","created":"2023-03-11","cited_by_count":12,"area":null},{"id":"2304.06537","title":"Transfer Knowledge from Head to Tail: Uncertainty Calibration under Long-tailed Distribution","created":"2023-04-13","cited_by_count":12,"area":null},{"id":"2106.14662","title":"Improving Uncertainty Calibration of Deep Neural Networks via Truth Discovery and Geometric Optimization","created":"2021-06-25","cited_by_count":11,"area":null},{"id":"2202.04348","title":"MBCT: Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration","created":"2022-02-09","cited_by_count":11,"area":null},{"id":"2308.11838","title":"A Benchmark Study on Calibration","created":"2023-08-23","cited_by_count":11,"area":null},{"id":"2312.08589","title":"Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors","created":"2023-12-14","cited_by_count":11,"area":null},{"id":"2411.02988","title":"Confidence Calibration of Classifiers with Many Classes","created":"2024-11-05","cited_by_count":11,"area":null},{"id":"2305.10886","title":"Minimum-Risk Recalibration of Classifiers","created":"2023-05-18","cited_by_count":10,"area":null},{"id":"2306.04985","title":"Beyond Probability Partitions: Calibrating Neural Networks with Semantic Aware Grouping","created":"2023-06-08","cited_by_count":10,"area":null},{"id":"2312.11858","title":"SimCalib: Graph Neural Network Calibration based on Similarity between Nodes","created":"2023-12-19","cited_by_count":10,"area":null},{"id":"2202.07679","title":"Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks","created":"2022-02-15","cited_by_count":9,"area":null},{"id":"2312.06645","title":"Beyond Classification: Definition and Density-based Estimation of Calibration in Object Detection","created":"2023-12-11","cited_by_count":9,"area":null},{"id":"2406.18763","title":"Conformalized Link Prediction on Graph Neural Networks","created":"2024-06-26","cited_by_count":9,"area":null},{"id":"2305.00070","title":"Online Platt Scaling with Calibeating","created":"2023-04-28","cited_by_count":8,"area":null},{"id":"2307.10440","title":"Confidence Estimation Using Unlabeled Data","created":"2023-07-19","cited_by_count":8,"area":null},{"id":"2308.12252","title":"How Safe Am I Given What I See? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy","created":"2023-08-23","cited_by_count":8,"area":null},{"id":"2401.03350","title":"Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks","created":"2024-01-07","cited_by_count":8,"area":null},{"id":"2406.04068","title":"Reassessing How to Compare and Improve the Calibration of Machine Learning Models","created":"2024-06-06","cited_by_count":8,"area":null},{"id":"2407.08649","title":"Confidence-based Estimators for Predictive Performance in Model Monitoring","created":"2024-07-11","cited_by_count":8,"area":null},{"id":"2410.19796","title":"Feature Clipping for Uncertainty Calibration","created":"2024-10-16","cited_by_count":8,"area":null},{"id":"2202.07825","title":"Reducing Overconfidence Predictions for Autonomous Driving Perception","created":"2022-02-16","cited_by_count":7,"area":null},{"id":"2306.10989","title":"Scaling of Class-wise Training Losses for Post-hoc Calibration","created":"2023-06-19","cited_by_count":7,"area":null},{"id":"2401.14487","title":"Neighbor-Aware Calibration of Segmentation Networks with Penalty-Based Constraints","created":"2024-01-25","cited_by_count":7,"area":null},{"id":"2405.14303","title":"Similarity-Navigated Conformal Prediction for Graph Neural Networks","created":"2024-05-23","cited_by_count":7,"area":null},{"id":"2410.04315","title":"Calibrating Expressions of Certainty","created":"2024-10-06","cited_by_count":7,"area":null},{"id":"2307.09858","title":"Towards Reliable Rare Category Analysis on Graphs via Individual Calibration","created":"2023-07-19","cited_by_count":6,"area":null},{"id":"2402.07632","title":"Understanding the Effects of Miscalibrated AI Confidence on User Trust, Reliance, and Decision Efficacy","created":"2024-02-12","cited_by_count":6,"area":null},{"id":"2402.10046","title":"How Flawed Is ECE? An Analysis via Logit Smoothing","created":"2024-02-15","cited_by_count":6,"area":null},{"id":"2501.02767","title":"Enhancing Trustworthiness of Graph Neural Networks with Rank-Based Conformal Training","created":"2025-01-06","cited_by_count":6,"area":null},{"id":"2504.03478","title":"Probabilistic Machine Learning for Noisy Labels in Earth Observation","created":"2025-04-04","cited_by_count":6,"area":null},{"id":"2605.27752","title":"Same Answer, Different Confidence: Protocol Sensitivity in LLM Confidence Calibration","created":"2026-05-26","cited_by_count":6,"area":null},{"id":"2406.00539","title":"CONFINE: Conformal Prediction for Interpretable Neural Networks","created":"2024-06-01","cited_by_count":5,"area":null},{"id":"2503.22725","title":"Uncertainty Weighted Gradients for Model Calibration","created":"2025-03-26","cited_by_count":5,"area":null},{"id":"2106.12070","title":"Towards Consistent Predictive Confidence through Fitted Ensembles","created":"2021-06-22","cited_by_count":4,"area":null},{"id":"2302.09369","title":"Calibrating the Rigged Lottery: Making All Tickets Reliable","created":"2023-02-18","cited_by_count":4,"area":null},{"id":"2401.09507","title":"Deep Ensemble Shape Calibration: Multi-Field Post-hoc Calibration in Online Advertising","created":"2024-01-17","cited_by_count":4,"area":null},{"id":"2402.17655","title":"Confidence-Aware Multi-Field Model Calibration","created":"2024-02-27","cited_by_count":4,"area":null},{"id":"2404.13016","title":"Optimizing Calibration by Gaining Aware of Prediction Correctness","created":"2024-04-19","cited_by_count":4,"area":null},{"id":"2406.06227","title":"PAC-Bayes Analysis for Recalibration in Classification","created":"2024-06-10","cited_by_count":4,"area":null},{"id":"2407.12330","title":"Uncertainty Calibration with Energy Based Instance-wise Scaling in the Wild Dataset","created":"2024-07-17","cited_by_count":4,"area":null},{"id":"2410.09570","title":"GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks","created":"2024-10-12","cited_by_count":4,"area":null},{"id":"2502.14268","title":"MCQA-Eval: Efficient Confidence Evaluation in NLG with Gold-Standard Correctness Labels","created":"2025-02-20","cited_by_count":4,"area":null},{"id":"2503.22163","title":"T-CIL: Temperature Scaling using Adversarial Perturbation for Calibration in Class-Incremental Learning","created":"2025-03-28","cited_by_count":4,"area":null},{"id":"2507.07780","title":"Where are we with calibration under dataset shift in image classification?","created":"2025-07-10","cited_by_count":4,"area":null},{"id":"2106.00638","title":"Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning","created":"2021-06-01","cited_by_count":3,"area":null},{"id":"2203.08958","title":"On the Usefulness of the Fit-on-the-Test View on Evaluating Calibration of Classifiers","created":"2022-03-16","cited_by_count":3,"area":null},{"id":"2210.03005","title":"To Softmax, or not to Softmax: that is the question when applying Active Learning for Transformer Models","created":"2022-10-06","cited_by_count":3,"area":null},{"id":"2307.13539","title":"Model Calibration in Dense Classification with Adaptive Label Perturbation","created":"2023-07-25","cited_by_count":3,"area":null},{"id":"2402.13765","title":"Accuracy-Preserving Calibration via Statistical Modeling on Probability Simplex","created":"2024-02-21","cited_by_count":3,"area":null},{"id":"2410.05407","title":"Improving Predictor Reliability with Selective Recalibration","created":"2024-10-07","cited_by_count":3,"area":null},{"id":"2410.12295","title":"Consistency Calibration: Improving Uncertainty Calibration via Consistency among Perturbed Neighbors","created":"2024-10-16","cited_by_count":3,"area":null},{"id":"2410.15241","title":"Conditional Uncertainty Quantification for Tensorized Topological Neural Networks","created":"2024-10-20","cited_by_count":3,"area":null},{"id":"2504.11434","title":"Enhancing Out-of-Distribution Detection with Extended Logit Normalization","created":"2025-04-15","cited_by_count":3,"area":null},{"id":"2506.23492","title":"Sample Margin-Aware Recalibration of Temperature Scaling","created":"2025-06-30","cited_by_count":3,"area":null},{"id":"2511.10439","title":"Improving Perturbation-based Explanations by Understanding the Role of Uncertainty Calibration","created":"2025-11-13","cited_by_count":3,"area":null},{"id":"2512.09054","title":"Improving Multi-Class Calibration through Normalization-Aware Isotonic Techniques","created":"2025-12-09","cited_by_count":3,"area":null},{"id":"2604.20614","title":"Too Sharp, Too Sure: When Calibration Follows Curvature","created":"2026-04-22","cited_by_count":3,"area":null},{"id":"2203.09278","title":"Confidence Calibration for Intent Detection via Hyperspherical Space and Rebalanced Accuracy-Uncertainty Loss","created":"2022-03-17","cited_by_count":2,"area":null},{"id":"2206.13071","title":"Uncertainty Calibration for Deep Audio Classifiers","created":"2022-06-27","cited_by_count":2,"area":null}],"next_cursor":"100"}