Copertina

The Limits of AI-Driven Allocation: Optimal Screening under Aleatoric Uncertainty

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Cortes-Gomez, Santiago, Rubio, Mateo Dulce, Patino, Carlos, Wilder, Bryan
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Healthcare LLM Benchmarks Are Only as Good as Their Explicit Assumptions
di: Raman, Naveen, et al.
Pubblicazione: (2026)

Auditing Fairness by Betting
di: Chugg, Ben, et al.
Pubblicazione: (2023)

When Agents Say One Thing and Do Another: Validating Elicited Beliefs from LLMs
di: Yamin, Khurram, et al.
Pubblicazione: (2026)

Credal Concept Bottleneck Models for Epistemic-Aleatoric Uncertainty Decomposition
di: Mukherjee, Tanmoy, et al.
Pubblicazione: (2026)

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models
di: Li, Ruiyang, et al.
Pubblicazione: (2026)

Credal Concept Bottleneck Models: Structural Separation of Epistemic and Aleatoric Uncertainty
di: Mukherjee, Tanmoy, et al.
Pubblicazione: (2026)

From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence
di: Wang, Tianyang, et al.
Pubblicazione: (2025)

Deep Modeling of Non-Gaussian Aleatoric Uncertainty
di: Acharya, Aastha, et al.
Pubblicazione: (2024)

Mind the Ambiguity: Aleatoric Uncertainty Quantification in LLMs for Safe Medical Question Answering
di: Liu, Yaokun, et al.
Pubblicazione: (2026)

DeepUQ: Assessing the Aleatoric Uncertainties from two Deep Learning Methods
di: Nevin, Rebecca, et al.
Pubblicazione: (2024)

Probabilistic Neural Networks (PNNs) for Modeling Aleatoric Uncertainty in Scientific Machine Learning
di: Pourkamali-Anaraki, Farhad, et al.
Pubblicazione: (2024)

HybridFlow: Quantification of Aleatoric and Epistemic Uncertainty with a Single Hybrid Model
di: Van Katwyk, Peter, et al.
Pubblicazione: (2025)

SpatialEpiBench: Benchmarking Spatial Information and Epidemic Priors in Forecasting
di: Lyu, Ruiqi, et al.
Pubblicazione: (2026)

Stochastic Actor-Critic: Mitigating Overestimation via Temporal Aleatoric Uncertainty
di: Özalp, Uğurcan
Pubblicazione: (2026)

Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matching
di: Van Nguyen, Phi, et al.
Pubblicazione: (2025)

Uncertainty-Aware Deep Learning Framework for Remaining Useful Life Prediction in Turbofan Engines with Learned Aleatoric Uncertainty
di: Sharma, Krishang
Pubblicazione: (2025)

How to Stay Curious while Avoiding Noisy TVs using Aleatoric Uncertainty Estimation
di: Mavor-Parker, Augustine N., et al.
Pubblicazione: (2021)

An AI-Based Public Health Data Monitoring System
di: Joshi, Ananya, et al.
Pubblicazione: (2025)

AI-Driven Optimization under Uncertainty for Mineral Processing Operations
di: Xu, William, et al.
Pubblicazione: (2025)

CUPID: A Plug-in Framework for Joint Aleatoric and Epistemic Uncertainty Estimation with a Single Model
di: Xu, Xinran, et al.
Pubblicazione: (2026)

Can LLMs Reconcile Knowledge Conflicts in Counterfactual Reasoning
di: Yamin, Khurram, et al.
Pubblicazione: (2025)

Rethinking Epistemic and Aleatoric Uncertainty for Active Open-Set Annotation: An Energy-Based Approach
di: Zong, Chen-Chen, et al.
Pubblicazione: (2025)

Disentangling Aleatoric and Epistemic Uncertainty in Physics-Informed Neural Networks. Application to Insulation Material Degradation Prognostics
di: Ramirez, Ibai, et al.
Pubblicazione: (2026)

Computationally Assisted Quality Control for Public Health Data Streams
di: Joshi, Ananya, et al.
Pubblicazione: (2023)

Understanding Reasoning in LLMs through Strategic Information Allocation under Uncertainty
di: Kim, Jeonghye, et al.
Pubblicazione: (2026)

Utility-Directed Conformal Prediction: A Decision-Aware Framework for Actionable Uncertainty Quantification
di: Cortes-Gomez, Santiago, et al.
Pubblicazione: (2024)

Federated Epidemic Surveillance
di: Lyu, Ruiqi, et al.
Pubblicazione: (2023)

Leaving the Nest: Going Beyond Local Loss Functions for Predict-Then-Optimize
di: Shah, Sanket, et al.
Pubblicazione: (2023)

IV Co-Scientist: Multi-Agent LLM Framework for Causal Instrumental Variable Discovery
di: Sheth, Ivaxi, et al.
Pubblicazione: (2026)

ScreenSearch: Uncertainty-Aware OS Exploration
di: Solodko, Michael, et al.
Pubblicazione: (2026)

Reinforcement learning with combinatorial actions for coupled restless bandits
di: Xu, Lily, et al.
Pubblicazione: (2025)

CAR-bench: Evaluating the Consistency and Limit-Awareness of LLM Agents under Real-World Uncertainty
di: Kirmayr, Johannes, et al.
Pubblicazione: (2026)

Outlier Ranking in Large-Scale Public Health Streams
di: Joshi, Ananya, et al.
Pubblicazione: (2024)

Knowledge Graph Representations for LLM-Based Policy Compliance Reasoning
di: Baldwin, Wilder, et al.
Pubblicazione: (2026)

An Epistemic and Aleatoric Decomposition of Arbitrariness to Constrain the Set of Good Models
di: Khan, Falaah Arif, et al.
Pubblicazione: (2023)

Edge-AI-Driven Learning-to-Rank for Decentralized Task Allocation in Circular Smart Manufacturing
di: Ghahramani, Mohammadhossein, et al.
Pubblicazione: (2026)

DAOpt: Modeling and Evaluation of Data-Driven Optimization under Uncertainty with LLMs
di: Zhu, WenZhuo, et al.
Pubblicazione: (2025)

Generative AI-assisted Participatory Modeling in Socio-Environmental Planning under Deep Uncertainty
di: Pei, Zhihao, et al.
Pubblicazione: (2026)

Preliminary Study of the Impact of AI-Based Interventions on Health and Behavioral Outcomes in Maternal Health Programs
di: Dasgupta, Arpan, et al.
Pubblicazione: (2024)

Valid Inference with Imperfect Synthetic Data
di: Byun, Yewon, et al.
Pubblicazione: (2025)