:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Bezirganyan, Grigor, Sellami, Sana, Berti-Équille, Laure, Fournier, Sébastien
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2412.18024
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal Data
by: Bezirganyan, Grigor, et al.
Published: (2024)

MixMAS: A Framework for Sampling-Based Mixer Architecture Search for Multimodal Fusion and Learning
by: Chergui, Abdelmadjid, et al.
Published: (2024)

Prior-Aligned Data Cleaning for Tabular Foundation Models
by: Berti-Equille, Laure
Published: (2026)

OrionBench: Benchmarking Time Series Generative Models in the Service of the End-User
by: Alnegheimish, Sarah, et al.
Published: (2023)

Large language models can be zero-shot anomaly detectors for time series?
by: Alnegheimish, Sarah, et al.
Published: (2024)

Explingo: Explaining AI Predictions using Large Language Models
by: Zytek, Alexandra, et al.
Published: (2024)

Hierarchical Classification for Automated Image Annotation of Coral Reef Benthic Structures
by: Blondin, Célia, et al.
Published: (2024)

Patient Trajectory Prediction: Integrating Clinical Notes with Transformers
by: Klioui, Sifal, et al.
Published: (2025)

Attribute Fusion-based Classifier on Framework of Belief Structure
by: Hu, Qiying, et al.
Published: (2025)

Uncertainty Quantification on Graph Learning: A Survey
by: Chen, Chao, et al.
Published: (2024)

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

Learning to Reason Efficiently with Discounted Reinforcement Learning
by: Ayoub, Alex, et al.
Published: (2025)

Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference
by: Reiser, Philipp, et al.
Published: (2023)

Multivariate and Online Transfer Learning with Uncertainty Quantification
by: Hickey, Jimmy, et al.
Published: (2024)

Uncertainty Quantification for Deep Learning
by: van Leeuwen, Peter Jan, et al.
Published: (2024)

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

A Survey on Uncertainty Quantification Methods for Deep Learning
by: He, Wenchong, et al.
Published: (2023)

Variational Neural Belief Parameterizations for Robust Dexterous Grasping under Multimodal Uncertainty
by: Enwerem, Clinton, et al.
Published: (2026)

Epistemic Wrapping for Uncertainty Quantification
by: Sultana, Maryam, et al.
Published: (2025)

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

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression
by: Bülte, Christopher, et al.
Published: (2025)

Reinforcement Learning with Quasi-Hyperbolic Discounting
by: Eshwar, S. R., et al.
Published: (2024)

Reducing Blackwell and Average Optimality to Discounted MDPs via the Blackwell Discount Factor
by: Grand-Clément, Julien, et al.
Published: (2023)

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

Uncertainty-Aware Reward Discounting for Mitigating Reward Hacking
by: Singha, Disha
Published: (2026)

Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?
by: Shen, Maohao, et al.
Published: (2024)

Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction
by: Ma, Ziqi, et al.
Published: (2024)

Uncertainty Quantification With Multiple Sources
by: Ying, Mufang, et al.
Published: (2024)

Discounted Adaptive Online Learning: Towards Better Regularization
by: Zhang, Zhiyu, et al.
Published: (2024)

Bridging the Gap Between Average and Discounted TD Learning
by: Tian, Haoxing, et al.
Published: (2026)

Uncertainty Quantification for Regression: A Unified Framework based on kernel scores
by: Bülte, Christopher, et al.
Published: (2025)

Uncertainty Quantification for In-Context Learning of Large Language Models
by: Ling, Chen, et al.
Published: (2024)

Multi-fidelity Machine Learning for Uncertainty Quantification and Optimization
by: Zhang, Ruda, et al.
Published: (2024)

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

Is the Last Layer Sufficient for Uncertainty Quantification?
by: Wilson, Joseph, et al.
Published: (2026)

Position: There Is No Free Bayesian Uncertainty Quantification
by: Melev, Ivan, et al.
Published: (2025)

Uncertainty Quantification for Motor Imagery BCI -- Machine Learning vs. Deep Learning
by: Suurmeijer, Joris, et al.
Published: (2025)

Can Uncertainty Quantification Improve Learned Index Benefit Estimation?
by: Yu, Tao, et al.
Published: (2024)

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning
by: Trivedi, Shubhendu, et al.
Published: (2025)

Probabilistic Consistency in Machine Learning and Its Connection to Uncertainty Quantification
by: Patrone, Paul, et al.
Published: (2025)