{"total":94,"rows":[{"id":"2209.02299","title":"A Survey of Machine Unlearning","created":"2022-09-06","cited_by_count":312,"area":"safety"},{"id":"2110.03051","title":"Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation","created":"2021-10-06","cited_by_count":71,"area":null},{"id":"2402.00809","title":"Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI","created":"2024-02-01","cited_by_count":62,"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":"2208.09052","title":"A Review of Uncertainty for Deep Reinforcement Learning","created":"2022-08-18","cited_by_count":40,"area":null},{"id":"2102.05375","title":"Strength of Minibatch Noise in SGD","created":"2021-02-10","cited_by_count":39,"area":null},{"id":"2109.06352","title":"Uncertainty-Aware Machine Translation Evaluation","created":"2021-09-13","cited_by_count":35,"area":null},{"id":"2306.05483","title":"On the Importance of Exploration for Generalization in Reinforcement Learning","created":"2023-06-08","cited_by_count":31,"area":null},{"id":"2306.11113","title":"Learn to Accumulate Evidence from All Training Samples: Theory and Practice","created":"2023-06-19","cited_by_count":29,"area":null},{"id":"2105.13283","title":"Deep Ensembles from a Bayesian Perspective","created":"2021-05-27","cited_by_count":27,"area":null},{"id":"2309.16314","title":"A Primer on Bayesian Neural Networks: Review and Debates","created":"2023-09-28","cited_by_count":27,"area":null},{"id":"2211.02231","title":"Residual Skill Policies: Learning an Adaptable Skill-based Action Space for Reinforcement Learning for Robotics","created":"2022-11-04","cited_by_count":24,"area":null},{"id":"2106.02993","title":"PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics","created":"2021-06-06","cited_by_count":21,"area":null},{"id":"2304.02902","title":"Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry","created":"2023-04-06","cited_by_count":21,"area":null},{"id":"2202.10847","title":"UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography","created":"2022-02-22","cited_by_count":19,"area":null},{"id":"2210.04994","title":"Sampling-based inference for large linear models, with application to linearised Laplace","created":"2022-10-10","cited_by_count":18,"area":null},{"id":"2405.04278","title":"Uncertainty Quantification Metrics for Deep Regression","created":"2024-05-07","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":"2409.02976","title":"Hallucination Detection in LLMs: Fast and Memory-Efficient Fine-Tuned Models","created":"2024-09-04","cited_by_count":15,"area":null},{"id":"2110.04020","title":"Pathologies in priors and inference for Bayesian transformers","created":"2021-10-08","cited_by_count":13,"area":null},{"id":"2312.02052","title":"DUCK: Distance-based Unlearning via Centroid Kinematics","created":"2023-12-04","cited_by_count":13,"area":"safety"},{"id":"2401.01484","title":"Uncertainty Regularized Evidential Regression","created":"2024-01-03","cited_by_count":13,"area":null},{"id":"2102.00902","title":"Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring","created":"2021-02-01","cited_by_count":11,"area":null},{"id":"2406.00332","title":"A Structured Review of Literature on Uncertainty in Machine Learning & Deep Learning","created":"2024-06-01","cited_by_count":10,"area":null},{"id":"2306.07541","title":"A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning","created":"2023-06-13","cited_by_count":8,"area":"offlinerl"},{"id":"2101.06417","title":"Bayesian Inference Forgetting","created":"2021-01-16","cited_by_count":7,"area":null},{"id":"2312.09454","title":"Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review","created":"2023-11-15","cited_by_count":7,"area":null},{"id":"2112.04153","title":"Model-Value Inconsistency as a Signal for Epistemic Uncertainty","created":"2021-12-08","cited_by_count":6,"area":null},{"id":"2108.02327","title":"PI3NN: Out-of-distribution-aware prediction intervals from three neural networks","created":"2021-08-05","cited_by_count":5,"area":null},{"id":"2109.09654","title":"Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?","created":"2021-09-20","cited_by_count":5,"area":null},{"id":"2201.09815","title":"Analytic Mutual Information in Bayesian Neural Networks","created":"2022-01-24","cited_by_count":5,"area":null},{"id":"2308.10650","title":"Deep Evidential Learning for Bayesian Quantile Regression","created":"2023-08-21","cited_by_count":5,"area":null},{"id":"2408.15136","title":"Low-Budget Simulation-Based Inference with Bayesian Neural Networks","created":"2024-08-27","cited_by_count":5,"area":null},{"id":"2104.10715","title":"Uncertainty-Aware Boosted Ensembling in Multi-Modal Settings","created":"2021-04-21","cited_by_count":4,"area":null},{"id":"2109.09048","title":"A framework for benchmarking uncertainty in deep regression","created":"2021-09-10","cited_by_count":4,"area":null},{"id":"2202.12653","title":"Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection","created":"2022-02-25","cited_by_count":4,"area":null},{"id":"2306.07235","title":"Deep Gaussian Mixture Ensembles","created":"2023-06-12","cited_by_count":4,"area":null},{"id":"2404.14642","title":"Uncertainty Quantification on Graph Learning: A Survey","created":"2024-04-23","cited_by_count":4,"area":null},{"id":"2105.09474","title":"Quantifying sources of uncertainty in drug discovery predictions with probabilistic models","created":"2021-05-18","cited_by_count":3,"area":null},{"id":"2105.14275","title":"Greedy Bayesian Posterior Approximation with Deep Ensembles","created":"2021-05-29","cited_by_count":3,"area":null},{"id":"2106.08750","title":"Quasi-Bayesian Dual Instrumental Variable Regression","created":"2021-06-16","cited_by_count":3,"area":null},{"id":"2110.13079","title":"Which Model to Trust: Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms for Continuous Control Tasks","created":"2021-10-25","cited_by_count":3,"area":null},{"id":"2202.10903","title":"Confident Neural Network Regression with Bootstrapped Deep Ensembles","created":"2022-02-22","cited_by_count":3,"area":null},{"id":"2202.12637","title":"Do autoencoders need a bottleneck for anomaly detection?","created":"2022-02-25","cited_by_count":3,"area":null},{"id":"2207.13730","title":"Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control","created":"2022-07-27","cited_by_count":3,"area":null},{"id":"2209.08418","title":"Sample-based Uncertainty Quantification with a Single Deterministic Neural Network","created":"2022-09-17","cited_by_count":3,"area":null},{"id":"2212.07118","title":"Uncertainty Quantification for Deep Neural Networks: An Empirical Comparison and Usage Guidelines","created":"2022-12-14","cited_by_count":3,"area":null},{"id":"2403.19381","title":"On Uncertainty Quantification for Near-Bayes Optimal Algorithms","created":"2024-03-28","cited_by_count":3,"area":null},{"id":"2405.01205","title":"Error-Driven Uncertainty Aware Training","created":"2024-05-02","cited_by_count":3,"area":null},{"id":"2407.00616","title":"DADEE: Well-calibrated uncertainty quantification in neural networks for barriers-based robot safety","created":"2024-06-30","cited_by_count":3,"area":null},{"id":"2412.05897","title":"Epistemic Uncertainty for Generated Image Detection","created":"2024-12-08","cited_by_count":3,"area":null},{"id":"2503.11339","title":"Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model","created":"2025-03-14","cited_by_count":3,"area":"offlinerl"},{"id":"2512.13746","title":"Probabilistic Predictions of Process-Induced Deformation in Carbon/Epoxy Composites Using a Deep Operator Network","created":"2025-12-15","cited_by_count":3,"area":null},{"id":"2206.13923","title":"SLOVA: Uncertainty Estimation Using Single Label One-Vs-All Classifier","created":"2022-06-28","cited_by_count":2,"area":null},{"id":"2211.08007","title":"Evidence-based Match-status-Aware Gait Recognition for Out-of-Gallery Gait Identification","created":"2022-11-15","cited_by_count":2,"area":null},{"id":"2212.05987","title":"Selective classification using a robust meta-learning approach","created":"2022-12-12","cited_by_count":2,"area":null},{"id":"2309.14240","title":"Learning to Abstain From Uninformative Data","created":"2023-09-25","cited_by_count":2,"area":null},{"id":"2401.13657","title":"Inadequacy of common stochastic neural networks for reliable clinical decision support","created":"2024-01-24","cited_by_count":2,"area":null},{"id":"2503.10873","title":"Mamba time series forecasting with uncertainty quantification","created":"2025-03-13","cited_by_count":2,"area":"ssm"},{"id":"2512.23753","title":"Generalized Regularized Evidential Deep Learning Models: Theory and Comprehensive Evaluation","created":"2025-12-27","cited_by_count":2,"area":null},{"id":"2202.04798","title":"Augmenting Neural Networks with Priors on Function Values","created":"2022-02-10","cited_by_count":1,"area":null},{"id":"2205.00163","title":"Deep Ensemble as a Gaussian Process Approximate Posterior","created":"2022-04-30","cited_by_count":1,"area":null},{"id":"2206.10261","title":"Interpretable Deep Causal Learning for Moderation Effects","created":"2022-06-21","cited_by_count":1,"area":null},{"id":"2406.03082","title":"Learning Solutions of Stochastic Optimization Problems with Bayesian Neural Networks","created":"2024-06-05","cited_by_count":1,"area":null},{"id":"2501.12314","title":"Uncertainty Quantification With Noise Injection in Neural Networks: A Bayesian Perspective","created":"2025-01-21","cited_by_count":1,"area":null},{"id":"2505.13585","title":"Scalable Bayesian Monte Carlo: fast uncertainty estimation beyond deep ensembles","created":"2025-05-19","cited_by_count":1,"area":null},{"id":"2103.01374","title":"A Kernel Framework to Quantify a Model's Local Predictive Uncertainty under Data Distributional Shifts","created":"2021-03-02","cited_by_count":0,"area":null},{"id":"2103.15682","title":"Rapid Risk Minimization with Bayesian Models Through Deep Learning Approximation","created":"2021-03-29","cited_by_count":0,"area":null},{"id":"2110.10038","title":"Coalitional Bayesian Autoencoders -- Towards explainable unsupervised deep learning","created":"2021-10-19","cited_by_count":0,"area":null},{"id":"2201.00649","title":"SAE: Sequential Anchored Ensembles","created":"2021-12-30","cited_by_count":0,"area":null},{"id":"2211.03148","title":"UATTA-ENS: Uncertainty Aware Test Time Augmented Ensemble for PIRC Diabetic Retinopathy Detection","created":"2022-11-06","cited_by_count":0,"area":null},{"id":"2303.04115","title":"Predicted Embedding Power Regression for Large-Scale Out-of-Distribution Detection","created":"2023-03-07","cited_by_count":0,"area":null},{"id":"2304.00709","title":"MSS-PAE: Saving Autoencoder-based Outlier Detection from Unexpected Reconstruction","created":"2023-04-03","cited_by_count":0,"area":null},{"id":"2304.04539","title":"UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensemble of BERTs for Classifying Common Mental Illnesses on Social Media Posts","created":"2023-04-10","cited_by_count":0,"area":null},{"id":"2307.06055","title":"Function-Space Regularization for Deep Bayesian Classification","created":"2023-07-12","cited_by_count":0,"area":null},{"id":"2307.12157","title":"Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach","created":"2023-07-22","cited_by_count":0,"area":null},{"id":"2308.12785","title":"Single-shot Bayesian approximation for neural networks","created":"2023-08-24","cited_by_count":0,"area":null},{"id":"2312.17411","title":"Generative Posterior Networks for Approximately Bayesian Epistemic Uncertainty Estimation","created":"2023-12-29","cited_by_count":0,"area":null},{"id":"2401.04933","title":"Rethinking Test-time Likelihood: The Likelihood Path Principle and Its Application to OOD Detection","created":"2024-01-10","cited_by_count":0,"area":null},{"id":"2404.06188","title":"Diverse Randomized Value Functions: A Provably Pessimistic Approach for Offline Reinforcement Learning","created":"2024-04-09","cited_by_count":0,"area":"offlinerl"},{"id":"2404.10314","title":"Awareness of uncertainty in classification using a multivariate model and multi-views","created":"2024-04-16","cited_by_count":0,"area":null},{"id":"2406.05469","title":"On Uniform, Bayesian, and PAC-Bayesian Deep Ensembles","created":"2024-06-08","cited_by_count":0,"area":null},{"id":"2408.06036","title":"Peaking into the Black-box: Prediction Intervals Give Insight into Data-driven Quadrotor Model Reliability","created":"2024-08-12","cited_by_count":0,"area":null},{"id":"2408.17098","title":"UTrack: Multi-Object Tracking with Uncertain Detections","created":"2024-08-30","cited_by_count":0,"area":null},{"id":"2409.05206","title":"SEF: A Method for Computing Prediction Intervals by Shifting the Error Function in Neural Networks","created":"2024-09-08","cited_by_count":0,"area":null},{"id":"2502.00197","title":"Learning Model Successors","created":"2025-01-31","cited_by_count":0,"area":null},{"id":"2504.01225","title":"A Conformal Risk Control Framework for Granular Word Assessment and Uncertainty Calibration of CLIPScore Quality Estimates","created":"2025-04-01","cited_by_count":0,"area":null},{"id":"2507.16796","title":"Uncertainty-Aware Knowledge Transformers for Peer-to-Peer Energy Trading with Multi-Agent Reinforcement Learning","created":"2025-07-22","cited_by_count":0,"area":null},{"id":"2508.07220","title":"Neural Bridge Processes","created":"2025-08-10","cited_by_count":0,"area":"diffusion"},{"id":"2511.06538","title":"Bayesian Uncertainty Quantification with Anchored Ensembles for Robust EV Power Consumption Prediction","created":"2025-11-09","cited_by_count":0,"area":null},{"id":"2602.16216","title":"UCTECG-Net: Uncertainty-aware Convolution Transformer ECG Network for Arrhythmia Detection","created":"2026-02-18","cited_by_count":0,"area":null},{"id":"2602.21408","title":"Generative Bayesian Computation as a Scalable Alternative to Gaussian Process Surrogates","created":"2026-02-24","cited_by_count":0,"area":null},{"id":"2607.06776","title":"Efficient Bayesian Deep Ensembles via Analytic Predictive Inference","created":"2026-07-07","cited_by_count":0,"area":null},{"id":"2607.28248","title":"Uncertainty quantification for trustworthy deep learning: Methods and measures","created":"2026-07-30","cited_by_count":0,"area":null}],"next_cursor":null}