Cover Image

Fair Uncertainty Quantification for Depression Prediction

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Yonghong, Zhang, Zheng, Zhou, Xiuzhuang
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Conformal Depression Prediction
by: Li, Yonghong, et al.
Published: (2024)

Tackling Incomplete Data in Air Quality Prediction: A Bayesian Deep Learning Framework for Uncertainty Quantification
by: Pian, Yuzhuang, et al.
Published: (2025)

Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction
by: Bethell, Daniel, et al.
Published: (2023)

Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models
by: Neumeier, Marion, et al.
Published: (2024)

Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification
by: Teufel, Jonas, et al.
Published: (2025)

UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
by: Yu, Dahai, et al.
Published: (2025)

Uncertainty Quantification in the Tsetlin Machine
by: Helin, Runar, et al.
Published: (2025)

Uncertainty Quantification in SVM prediction
by: Anand, Pritam
Published: (2025)

The Role of Ambiguity in Error Prediction via Uncertainty Quantification
by: Staliūnaitė, Ieva Raminta, et al.
Published: (2026)

Credal Ensemble Distillation for Uncertainty Quantification
by: Wang, Kaizheng, et al.
Published: (2025)

Multimodal Gender Fairness in Depression Prediction: Insights on Data from the USA & China
by: Cameron, Joseph, et al.
Published: (2024)

Torch-Uncertainty: A Deep Learning Framework for Uncertainty Quantification
by: Lafage, Adrien, et al.
Published: (2025)

Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents
by: Kiyani, Shayan, et al.
Published: (2025)

Physics-Based Hybrid Machine Learning for Critical Heat Flux Prediction with Uncertainty Quantification
by: Furlong, Aidan, et al.
Published: (2025)

Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning Approach
by: Gharsallaoui, Mohammed Amine, et al.
Published: (2024)

PerFlow: Physics-Embedded Rectified Flow for Efficient Reconstruction and Uncertainty Quantification of Spatiotemporal Dynamics
by: Zhou, Hao, et al.
Published: (2026)

Conf-Gen: Conformal Uncertainty Quantification for Generative Models
by: Loaiza-Ganem, Gabriel, et al.
Published: (2026)

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

Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests
by: Nolte, Daniel, et al.
Published: (2024)

Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution
by: Wu, Tailin, et al.
Published: (2024)

Conformal Prediction Beyond the Seen: A Missing Mass Perspective for Uncertainty Quantification in Generative Models
by: Noorani, Sima, et al.
Published: (2025)

GNN's Uncertainty Quantification using Self-Distillation
by: Daneshvar, Hirad, et al.
Published: (2025)

Tackling Fake Forgetting through Uncertainty Quantification
by: Shi, Yingdan, et al.
Published: (2025)

Towards Uncertainty Quantification in Generative Model Learning
by: Morales, Giorgio, et al.
Published: (2025)

A Rate-Distortion View of Uncertainty Quantification
by: Apostolopoulou, Ifigeneia, et al.
Published: (2024)

Uncertainty Quantification via Stable Distribution Propagation
by: Petersen, Felix, et al.
Published: (2024)

Robust Uncertainty Quantification for Self-Evolving Large Language Models via Continual Domain Pretraining
by: Zhou, Xiaofan, et al.
Published: (2025)

Structure-Aware Epistemic Uncertainty Quantification for Neural Operator PDE Surrogates
by: Song, Haoze, et al.
Published: (2026)

Shifting Uncertainty to Critical Moments: Towards Reliable Uncertainty Quantification for VLA Model
by: Tang, Yanchuan, et al.
Published: (2026)

DiffLoad: Uncertainty Quantification in Electrical Load Forecasting with the Diffusion Model
by: Wang, Zhixian, et al.
Published: (2023)

Uncertainty Quantification for Machine Learning in Healthcare: A Survey
by: López, L. Julián Lechuga, et al.
Published: (2025)

Model-Agnostic Dynamic Feature Selection with Uncertainty Quantification
by: Fumanal-Idocin, Javier, et al.
Published: (2025)

Quantile-Free Uncertainty Quantification in Graph Neural Networks
by: park, Soyoung, et al.
Published: (2026)

Uncertainty Quantification for Gradient-based Explanations in Neural Networks
by: Mulye, Mihir, et al.
Published: (2024)

Improving Label Error Detection and Elimination with Uncertainty Quantification
by: Jakubik, Johannes, et al.
Published: (2024)

Quantification of Credal Uncertainty: A Distance-Based Approach
by: Gonzalez-Garcia, Xabier, et al.
Published: (2026)

Explainable Uncertainty Quantification for Wastewater Treatment Energy Prediction via Interval Type-2 Neuro-Fuzzy System
by: Khaled, Qusai, et al.
Published: (2026)

Zono-Conformal Prediction: Zonotope-Based Uncertainty Quantification for Regression and Classification Tasks
by: Lützow, Laura, et al.
Published: (2025)

Functional Entropy: Predicting Functional Correctness in LLM-Generated Code with Uncertainty Quantification
by: Bouchard, Dylan, et al.
Published: (2026)

Uncertainty Quantification for Surface Ozone Emulators using Deep Learning
by: Doerksen, Kelsey, et al.
Published: (2025)