Data to 7 Oct 2026

Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD Detection On Medical Tabular Data

arXiv 2011.03274 ↗First posted 6 Nov 2020cs.LG cs.AI stat.MLcited by 52

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Abstract (arXiv)

When deploying machine learning models in high-stakes real-world environments such as health care, it is crucial to accurately assess the uncertainty concerning a model's prediction on abnormal inputs. However, there is a scarcity of literature analyzing this problem on medical data, especially on mixed-type tabular data such as Electronic Health Records. We close this gap by presenting a series of tests including a large variety of contemporary uncertainty estimation techniques, in order to determine whether they are able to identify out-of-distribution (OOD) patients. In contrast to previous work, we design tests on realistic and clinically relevant OOD groups, and run experiments on real-world medical data. We find that almost all techniques fail to achieve convincing results, partly disagreeing with earlier findings.

Key facts. Cited by 52 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Nov 2020.

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Citing papers per year: 2021: 6; 2022: 3; 2023: 14; 2024: 15; 2025: 9; 2026: 5.

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2021Deep Neural Networks and Tabular Data: A Surveycited by 489
2023Generating with Confidence: Uncertainty Quantification for Black-box Large Language Modelscited by 286
2023When Do Neural Nets Outperform Boosted Trees on Tabular Data?cited by 218
2021Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimationcited by 71
2024Calibrating Large Language Models Using Their Generations Onlycited by 63
2024Retrieval & Fine-Tuning for In-Context Tabular Modelscited by 58
2023STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tablescited by 48
2024TuneTables: Context Optimization for Scalable Prior-Data Fitted Networkscited by 47
2023Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detectioncited by 29
2024TabPFGen -- Tabular Data Generation with TabPFNcited by 26
2022Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcitycited by 24
2024MetaOOD: Automatic Selection of OOD Detection Modelscited by 16

38 more

2021Out-of-Distribution Detection for Medical Applications: Guidelines for Practical Evaluationcited by 15
2022JAWS: Auditing Predictive Uncertainty Under Covariate Shiftcited by 14
2024Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Datacited by 11
2023GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Datacited by 10
2024TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-casescited by 10
2024Tabular Transfer Learning via Prompting LLMscited by 10
2023Unmasking the Chameleons: A Benchmark for Out-of-Distribution Detection in Medical Tabular Datacited by 8
2025Doubling Your Data in Minutes: Ultra-fast Tabular Data Generation via LLM-Induced Dependency Graphscited by 8
2024Understanding and Mitigating Memorization in Diffusion Models for Tabular Datacited by 7
2025Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak Learnerscited by 7
2021Benchmarking Uncertainty Quantification on Biosignal Classification Tasks under Dataset Shiftcited by 6
2023Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantificationcited by 6
2023Distribution-free risk assessment of regression-based machine learning algorithmscited by 6
2023Generating tabular datasets under differential privacycited by 5
2024How Useful is Continued Pre-Training for Generative Unsupervised Domain Adaptation?cited by 5
2025M3OOD: Automatic Selection of Multimodal OOD Detectorscited by 5
2021BEDS-Bench: Behavior of EHR-models under Distributional Shift--A Benchmarkcited by 4
2023TT-TFHE: a Torus Fully Homomorphic Encryption-Friendly Neural Network Architecturecited by 4
2023Homological Convolutional Neural Networkscited by 4
2023Can Pre-trained Networks Detect Familiar Out-of-Distribution Data?cited by 4
2024TabKANet: Tabular Data Modeling with Kolmogorov-Arnold Network and Transformercited by 4
2025Outlier Synthesis via Hamiltonian Monte Carlo for Out-of-Distribution Detectioncited by 4
2026FORESTLLM: Large Language Models Make Random Forest Great on Few-shot Tabular Learningcited by 4
2021Bi-Discriminator Class-Conditional Tabular GANcited by 3
2023MLHOps: Machine Learning for Healthcare Operationscited by 2
2024Approximations to the Fisher Information Metric of Deep Generative Models for Out-Of-Distribution Detectioncited by 2
2025LLM Empowered Prototype Learning for Zero and Few-Shot Tasks on Tabular Datacited by 2
2023Constrained Adaptive Attacks: Realistic Evaluation of Adversarial Examples and Robust Training of Deep Neural Networks for Tabular Datacited by 1
2024Mitigating Overconfidence in Out-of-Distribution Detection by Capturing Extreme Activationscited by 1
2024A Neural Network Alternative to Tree-based Modelscited by 1
2025GLIP-OOD: Zero-Shot Graph OOD Detection with Graph Foundation Modelcited by 1
2026An Isotropic Approach to Efficient Uncertainty Quantification with Gradient Normscited by 1
2026RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databasescited by 1
2022Simple Calibration via Geodesic Kernelscited by 0
2024VI-OOD: A Unified Representation Learning Framework for Textual Out-of-distribution Detectioncited by 0
2025How to RETIRE Tabular Data in Favor of Discrete Digital Signal Representationcited by 0
2025MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domainscited by 0
2025CatBack: Universal Backdoor Attacks on Tabular Data via Categorical Encodingcited by 0

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