{"total":50,"rows":[{"id":"2107.05719","title":"Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration","created":"2021-07-12","cited_by_count":79,"area":null},{"id":"2206.15474","title":"Forecasting Future World Events with Neural Networks","created":"2022-06-30","cited_by_count":63,"area":null},{"id":"2102.06289","title":"When and How Mixup Improves Calibration","created":"2021-02-11","cited_by_count":60,"area":null},{"id":"2109.10254","title":"Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification","created":"2021-09-21","cited_by_count":57,"area":null},{"id":"2203.01255","title":"Low-Degree Multicalibration","created":"2022-03-02","cited_by_count":45,"area":null},{"id":"2108.01846","title":"Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violations","created":"2021-08-04","cited_by_count":39,"area":null},{"id":"2302.11874","title":"What Can We Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers","created":"2023-02-23","cited_by_count":35,"area":null},{"id":"2208.12084","title":"Calibrated Selective Classification","created":"2022-08-25","cited_by_count":33,"area":null},{"id":"2306.02738","title":"A Large-Scale Study of Probabilistic Calibration in Neural Network Regression","created":"2023-06-05","cited_by_count":27,"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":"2310.20211","title":"Calibration by Distribution Matching: Trainable Kernel Calibration Metrics","created":"2023-10-31","cited_by_count":20,"area":null},{"id":"2202.01147","title":"Improving Screening Processes via Calibrated Subset Selection","created":"2022-02-02","cited_by_count":16,"area":null},{"id":"2106.03395","title":"How to Evaluate Uncertainty Estimates in Machine Learning for Regression?","created":"2021-06-07","cited_by_count":15,"area":null},{"id":"2105.04471","title":"Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family Distributions","created":"2021-05-10","cited_by_count":13,"area":null},{"id":"2309.06240","title":"Calibration in Machine Learning Uncertainty Quantification: beyond consistency to target adaptivity","created":"2023-09-12","cited_by_count":13,"area":null},{"id":"2505.21460","title":"High-Dimensional Calibration from Swap Regret","created":"2025-05-27","cited_by_count":13,"area":null},{"id":"2402.07357","title":"Regression Trees for Fast and Adaptive Prediction Intervals","created":"2024-02-12","cited_by_count":12,"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":"2206.00074","title":"To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontier","created":"2022-05-31","cited_by_count":11,"area":null},{"id":"2306.14916","title":"Uncertainty Estimation for Molecules: Desiderata and Methods","created":"2023-06-20","cited_by_count":11,"area":null},{"id":"2112.04620","title":"Online Calibrated and Conformal Prediction Improves Bayesian Optimization","created":"2021-12-08","cited_by_count":10,"area":null},{"id":"2205.14568","title":"Towards Instance-Wise Calibration: Local Amortized Diagnostics and Reshaping of Conditional Densities (LADaR)","created":"2022-05-29","cited_by_count":10,"area":null},{"id":"2302.02595","title":"Clarifying Trust of Materials Property Predictions using Neural Networks with Distribution-Specific Uncertainty Quantification","created":"2023-02-06","cited_by_count":8,"area":null},{"id":"2402.08383","title":"Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution","created":"2024-02-13","cited_by_count":8,"area":null},{"id":"2405.11533","title":"Hierarchical Selective Classification","created":"2024-05-19","cited_by_count":7,"area":null},{"id":"2307.15247","title":"Is this model reliable for everyone? Testing for strong calibration","created":"2023-07-28","cited_by_count":6,"area":null},{"id":"2403.11964","title":"Probabilistic Calibration by Design for Neural Network Regression","created":"2024-03-18","cited_by_count":6,"area":null},{"id":"2504.18395","title":"Three Types of Calibration with Properties and their Semantic and Formal Relationships","created":"2025-04-25","cited_by_count":5,"area":null},{"id":"2205.01040","title":"A Survey on Uncertainty Toolkits for Deep Learning","created":"2022-05-02","cited_by_count":4,"area":null},{"id":"2403.02998","title":"Towards Calibrated Deep Clustering Network","created":"2024-03-04","cited_by_count":4,"area":null},{"id":"2409.19157","title":"Calibrated Probabilistic Forecasts for Arbitrary Sequences","created":"2024-09-27","cited_by_count":4,"area":null},{"id":"2305.12283","title":"Distribution-Free Model-Agnostic Regression Calibration via Nonparametric Methods","created":"2023-05-20","cited_by_count":3,"area":null},{"id":"2505.16636","title":"Multivariate Latent Recalibration for Conditional Normalizing Flows","created":"2025-05-22","cited_by_count":3,"area":null},{"id":"2508.09732","title":"Predictive Uncertainty for Runtime Assurance of a Real-Time Computer Vision-Based Landing System","created":"2025-08-13","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":"2305.18655","title":"Parity Calibration","created":"2023-05-29","cited_by_count":2,"area":null},{"id":"2406.06968","title":"Beyond the Norms: Detecting Prediction Errors in Regression Models","created":"2024-06-11","cited_by_count":2,"area":null},{"id":"2202.01840","title":"Hidden Heterogeneity: When to Choose Similarity-Based Calibration","created":"2022-02-03","cited_by_count":1,"area":null},{"id":"2211.14545","title":"Ensemble Multi-Quantiles: Adaptively Flexible Distribution Prediction for Uncertainty Quantification","created":"2022-11-26","cited_by_count":1,"area":null},{"id":"2212.12658","title":"Improving Uncertainty Quantification of Variance Networks by Tree-Structured Learning","created":"2022-12-24","cited_by_count":1,"area":null},{"id":"2307.09929","title":"Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation","created":"2023-07-19","cited_by_count":1,"area":null},{"id":"2508.17761","title":"Evaluating the Quality of the Quantified Uncertainty for (Re)Calibration of Data-Driven Regression Models","created":"2025-08-25","cited_by_count":1,"area":null},{"id":"2604.20409","title":"Calibrating conditional risk","created":"2026-04-22","cited_by_count":1,"area":null},{"id":"2111.10847","title":"Diffusion Tensor Estimation with Uncertainty Calibration","created":"2021-11-21","cited_by_count":0,"area":null},{"id":"2312.07952","title":"Meta-learning to Calibrate Gaussian Processes with Deep Kernels for Regression Uncertainty Estimation","created":"2023-12-13","cited_by_count":0,"area":null},{"id":"2409.10855","title":"Calibrated Multivariate Regression with Localized PIT Mappings","created":"2024-09-17","cited_by_count":0,"area":null},{"id":"2506.19630","title":"Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations","created":"2025-06-24","cited_by_count":0,"area":null},{"id":"2510.21273","title":"Enforcing Calibration in Multi-Output Probabilistic Regression with Pre-rank Regularization","created":"2025-10-24","cited_by_count":0,"area":null},{"id":"2601.21455","title":"Questioning the Coverage-Length Metric in Conformal Prediction: When Shorter Intervals Are Not Better","created":"2026-01-29","cited_by_count":0,"area":null}],"next_cursor":null}