Non Gaussian Aleatoric Uncertainty

Non-Gaussian aleatoric uncertainty focuses on quantifying uncertainty inherent in the data itself, particularly when it deviates from the typical assumption of a Gaussian distribution. Current research emphasizes developing deep learning methods, including normalizing flows and ensembles, to model these complex, non-Gaussian uncertainty patterns, often in conjunction with techniques to disentangle aleatoric uncertainty from epistemic uncertainty (uncertainty due to lack of knowledge). This work is crucial for improving the reliability and safety of machine learning models in various applications, from robotics and autonomous systems to medical diagnosis and scientific simulations, where accurate uncertainty quantification is paramount for trustworthy decision-making.

Papers

September 12, 2024

Quantifying Aleatoric and Epistemic Dynamics Uncertainty via Local Conformal Calibration
Luís Marques, Dmitry Berenson
Epistemic Uncertainty Aleatoric Uncertainty Conformal Regression Non Gaussian Aleatoric Uncertainty

June 4, 2024

Label-wise Aleatoric and Epistemic Uncertainty Quantification
Yusuf Sale, Paul Hofman, Timo Löhr, Lisa Wimmer, Thomas Nagler, Eyke Hüllermeier
Uncertainty Quantification Label Uncertainty Uncertainty Measure Epistemic Uncertainty Quantification Non Gaussian Aleatoric Uncertainty

May 30, 2024

Deep Modeling of Non-Gaussian Aleatoric Uncertainty
Aastha Acharya, Caleb Lee, Marissa D'Alonzo, Jared Shamwell, Nisar R. Ahmed, Rebecca Russell
Deep Model State Estimation Aleatoric Uncertainty Conditional Density Non Gaussian Non Gaussian Aleatoric Uncertainty

April 18, 2024

Quantifying Aleatoric and Epistemic Uncertainty with Proper Scoring Rules
Paul Hofman, Yusuf Sale, Eyke Hüllermeier
Loss Function Epistemic Uncertainty Aleatoric Uncertainty Relative Probability Scoring Rule Non Gaussian Aleatoric Uncertainty Uncertainty Representation

July 5, 2023

A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty
Atul Agrawal, Phaedon-Stelios Koutsourelakis
Model Uncertainty Aleatoric Uncertainty Closure Model Non Gaussian Aleatoric Uncertainty Reynolds Averaged Navier Stokes Reynolds Stress

May 10, 2023

Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces
Jonas Busk, Mikkel N. Schmidt, Ole Winther, Tejs Vegge, Peter Bjørn Jørgensen
Epistemic Uncertainty Molecular Dynamic Aleatoric Uncertainty Energy Policy Research Uncertainty Calibration Interatomic Potential External Human Force Non Gaussian Aleatoric Uncertainty

February 17, 2023

Learning to Forecast Aleatoric and Epistemic Uncertainties over Long Horizon Trajectories
Aastha Acharya, Rebecca Russell, Nisar R. Ahmed
LeArning Abstract Autonomous Agent World Model Epistemic Uncertainty Agent Trajectory Non Gaussian Aleatoric Uncertainty Long Horizon Trajectory

February 2, 2023

Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling
Lucas Berry, David Meger
Normalizing Flow Epistemic Uncertainty Aleatoric Uncertainty Structured Distribution Non Gaussian Aleatoric Uncertainty

January 27, 2023

Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions
Hao Wang, Luxi He, Rui Gao, Flavio P. Calmon
Machine Learning Fairness Constraint Fundamental Limit Fairness Intervention Epistemic State Non Gaussian Aleatoric Uncertainty

September 7, 2022

Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures?
Lisa Wimmer, Yusuf Sale, Paul Hofman, Bern Bischl, Eyke Hüllermeier
Machine Learning Uncertainty Quantification Mutual Information Epistemic Uncertainty Information Theory Conditional Entropy Non Gaussian Aleatoric Uncertainty

May 20, 2022

A General Framework for quantifying Aleatoric and Epistemic uncertainty in Graph Neural Networks
Sai Munikoti, Deepesh Agarwal, Laya Das, Balasubramaniam Natarajan
Graph Neural Network Epistemic Uncertainty General Framework Aleatoric Uncertainty Non Gaussian Aleatoric Uncertainty

April 20, 2022

A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglement
Matias Valdenegro-Toro, Daniel Saromo
Uncertainty Quantification Epistemic Uncertainty Aleatoric Uncertainty Closer Look Non Gaussian Aleatoric Uncertainty Uncertainty Disentanglement

Non Gaussian Aleatoric Uncertainty

Papers

Quantifying Aleatoric and Epistemic Dynamics Uncertainty via Local Conformal Calibration

Label-wise Aleatoric and Epistemic Uncertainty Quantification

Deep Modeling of Non-Gaussian Aleatoric Uncertainty

Quantifying Aleatoric and Epistemic Uncertainty with Proper Scoring Rules

A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty

Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces

Learning to Forecast Aleatoric and Epistemic Uncertainties over Long Horizon Trajectories

Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling

Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions

Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures?

A General Framework for quantifying Aleatoric and Epistemic uncertainty in Graph Neural Networks

A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglement