CLEAR calibrates both aleatoric and epistemic uncertainties for better predictive intervals.
problem Balanced uncertainty quantification for reliable predictive modeling.
method CLEAR uses two parameters, γ1 and γ2, to combine aleatoric and epistemic uncertainties.
result Clear achieves significant improvements in interval width and coverage.
We propose orthogonality as a necessary condition for disentangling aleatoric and epistemic uncertainty.
problem Jointly estimating aleatoric and epistemic uncertainty is problematic and non-trivial.
method We propose orthogonality as a necessary condition for disentanglement and construct UDE to measure orthogonality and consistency.
result Orthogonality and consistency are necessary and sufficient criteria for disentanglement.
Bayesian neural networks decompose uncertainty into epistemic and aleatoric components for efficient and risk-sensitive learning.
problem Uncertainty in Bayesian neural networks estimation of weights and complex noise patterns in data.
method Decomposition of uncertainty into epistemic and aleatoric components, and definition of a risk-sensitive criterion for reinforcement learning.
result Identification of informative points for active learning and policies balancing expected cost, model-bias, and noise aversion.
Framework disentangles deep feature uncertainty for efficient inference.
problem Inference-time uncertainty estimation for reliable decision-making.
method Uncertainty-Guided Inference-Time Selection framework.
result Significantly tighter prediction intervals and 60% compute reduction.
Model separates overall uncertainty into aleatoric and epistemic components for active learning.
problem Active learning with uncertainty quantification.
method Non-stationary Heteroscedastic Gaussian process model.
result Model separates overall uncertainty into aleatoric and epistemic components.
The study quantifies and compares aleatoric and epistemic discrimination in ML models.
problem Sources of discrimination in ML models and their impact on performance.
method Quantifying aleatoric and epistemic discrimination using statistical experiments and model accuracy.
result State-of-the-art fairness interventions are effective at removing epistemic discrimination but not aleatoric discrimination in datasets with missing values.
Bayesian method detects outliers and uncertain points in data.
problem Detecting outliers and uncertain points in data using Bayesian methods.
method Generative model of data curation for aleatoric uncertainty, combining with epistemic uncertainty and outlier exposure.
result Principled Bayesian approach outperforms methods using aleatoric or epistemic uncertainty alone.
CLAPS improves conformal regression by adaptively scaling interval widths based on last-layer Laplace uncertainty.
problem Lack of adaptive interval width scaling in conformal regression for heterogeneous inputs.
method CLAPS uses heteroscedastic last-layer Laplace uncertainty to adaptively scale interval widths, combining aleatoric and epistemic uncertainties.
result CLAPS provides competitive interval efficiency with nominal-level coverage, reducing to aleatoric scaling as epistemic uncertainty decreases.
The paper quantifies aleatoric and epistemic uncertainties with random forests.
problem Addressing uncertainty in machine learning predictions.
method Using decision trees and random forests to measure aleatoric and epistemic uncertainties.
result Random forests effectively quantify uncertainties compared to deep neural networks.
Cooperative model disentangles data uncertainties.
problem Disentangling aleatoric and epistemic uncertainties in real-world data.
method Cooperatively trains a variance estimation network with a Bayesian neural network.
result Improves mean estimation and disentangles uncertainties.
Proposes a new principle for active learning based on epistemic uncertainty.
problem Active learning and uncertainty quantification in machine learning.
method Distinction between epistemic and aleatoric uncertainty; proposes epistemic uncertainty sampling.
result Epistemic uncertainty sampling shows promising performance in experimental studies.
Bayesian inference improves neural network predictions by separating aleatoric and epistemic uncertainties.
problem Improving prediction accuracy of neural networks by quantifying and separating uncertainties.
method Approximated posterior distributions using deep ensembles for various neural network architectures.
result Prediction accuracy depends on both aleatoric and epistemic uncertainties, not just marginalized uncertainty.
The paper introduces new measures for quantifying uncertainty in machine learning.
problem Uncertainty representation and quantification in machine learning.
method Proper scoring rules for aleatoric and epistemic uncertainty quantification.
result Established a natural bridge between credal set and second-order distribution representations of uncertainty.
Bayesian neural networks decompose uncertainty into epistemic and aleatoric components.
problem Uncertainty in Bayesian neural networks with latent variables.
method Information theoretic approach and risk-sensitive objective for safe reinforcement learning.
result Natural decomposition of predictive uncertainty in Bayesian active learning and safe RL.
Decoupled PFNs improve sequential decision-making by separating epistemic and aleatoric uncertainties.
problem Sequential decision-making requires distinguishing between epistemic uncertainty about latent signals and irreducible aleatoric observation noise.
method Developed a decoupled PFN architecture that uses query-level labels to train separate heads for latent signal and aleatoric noise.
result Empirically, decoupled PFNs mitigate the failure mode of total-variance exploration in noisy and heteroscedastic settings.
JUCAL jointly calibrates aleatoric and epistemic uncertainties in classifier ensembles.
problem Misrepresentation of predictive uncertainty due to unbalanced aleatoric and epistemic uncertainties.
method Joint Uncertainty Calibration (JUCAL) that jointly calibrates two constants to weight and scale uncertainties.
result Significantly outperforms state-of-the-art calibration methods across various text classification tasks.
Deep ensembles effectively capture epistemic uncertainty through training stochasticity, providing a frequentist perspective.
problem Understanding and quantifying epistemic uncertainty in machine learning models.
method Bootstrap-based estimator and decomposition of deep ensembles into data variability and training stochasticity.
result Deep ensembles primarily capture training stochasticity, explaining their effectiveness in quantifying epistemic uncertainty.
Uncertainty modeling for dynamical systems
problem Uncertainty modeling for dynamical systems
method Discussing sources of uncertainty, their nature, and task-specific objectives
result Identifying the types of uncertainty needed for dynamical systems
This paper introduces the distinction between aleatoric and epistemic uncertainty in machine learning.
problem The need to distinguish between types of uncertainty in machine learning.
method Introduction and overview of existing methods.
result The distinction between aleatoric and epistemic uncertainty.
DER uses neural nets to better handle uncertainty in machine learning.
problem Need for principled uncertainty reasoning in safety-critical domains.
method Uncertainty-aware regression-based neural networks (NNs) with evidential distributions.
result DER shows promise over traditional methods but is a heuristic.
New method extracts aleatoric and epistemic uncertainties from regression-based neural networks.
problem Need for principled uncertainty reasoning in machine learning systems.
method Learning evidential distributions for aleatoric and epistemic uncertainties.
result Allows for the simultaneous extraction of both uncertainties without sampling or out-of-distribution data.
A framework for estimating both epistemic and aleatoric uncertainties in reinforcement learning.
problem Estimating risk and uncertainty in deep reinforcement learning.
method Proposed a framework for disentangling and estimating epistemic and aleatoric uncertainties on learned Q-values, derived unbiased estimators, and introduced an uncertainty-aware DQN algorithm.
result The uncertainty-aware DQN algorithm exhibits safe learning behavior and outperforms other DQN variants on the MinAtar testbed.
Paper decomposes risk into aleatoric and epistemic uncertainties and generates predictive uncertainty measures.
problem Unclear relationships between various predictive uncertainty measures in literature.
method Bayesian estimation to decompose risk into aleatoric and epistemic uncertainties, generating different predictive uncertainty measures.
result Experimental validation confirms usefulness of derived predictive uncertainty measures for detecting out-of-distribution and misclassified instances.
The paper extends calibration to sets of probabilistic classifiers, finding many ensembles are poorly calibrated.
problem Evaluating the validity of epistemic uncertainty in sets of probabilistic classifiers.
method Proposed a novel nonparametric calibration test for sets of probabilistic classifiers.
result Ensembles of deep neural networks are often not well calibrated.
A new method decomposes subjective risk into epistemic and aleatoric uncertainties.
problem Uncertainty quantification in modeling decisions.
method Subjective risk decomposition using strictly proper loss.
result Recovery of classic uncertainty measures and new learning-theoretic connections.
New method estimates model uncertainty in regression.
problem Challenges in distinguishing aleatoric and epistemic uncertainty.
method Conditional predictions with model's initial output.
result Rigorous frequentist approach to epistemic uncertainty.
Second-order methods fail to fully quantify epistemic uncertainty, leading to biased predictions.
problem Incomplete quantification of epistemic uncertainty in machine learning models.
method Analysis of existing second-order uncertainty estimation methods.
result Current methods overestimate aleatoric uncertainty and underestimate epistemic uncertainty, leading to biased predictions.
Method provides formal guarantees for decomposing model uncertainty.
problem Decomposing model uncertainty into aleatoric and epistemic components.
method Higher-order calibration using k-snapshots.
result Formal guarantees for aleatoric uncertainty matching real-world distribution.
The paper critiques existing uncertainty concepts and proposes a new decision-theoretic approach.
problem Incoherence in existing discussions of aleatoric and epistemic uncertainty.
method Decision-theoretic perspective that relates uncertainty, predictive performance, and statistical dispersion.
result Popular information-theoretic quantities can be poor estimators but still useful for guiding data acquisition.
Extends neural network training framework to handle noise and uncertainty.
problem Handling noise and uncertainty in neural network training.
method Integrates non-zero aleatoric noise and derives posterior covariance for epistemic uncertainty.
result Derives an estimator for posterior covariance, providing a handle on epistemic uncertainty.
Unified Bayesian framework for quantifying GNN uncertainty.
problem Quantifying uncertainty in GNN predictions due to modeling errors and measurement uncertainty.
method Unified Bayesian framework with aleatoric uncertainty from probabilistic links and feature noise, and epistemic uncertainty from model parameter distribution. Uses Assumed Density Filtering for aleatoric uncertainty and Monte Carlo dropout for model parameter uncertainty.
result Bayesian model performs similarly to frequentist model and provides additional uncertainty information.
Softplus normalization improves uncertainty estimation in Bayesian CNNs.
problem Improving uncertainty estimation in Bayesian CNNs.
method Softplus normalization applied to the final layer of Bayesian CNNs with variational inference.
result The method achieves equivalent performance to frequentist inference while naturally incorporating uncertainty and regularization.
This study examines how neural network latent representations correlate with model uncertainty.
problem Detecting model uncertainty in neural networks.
method Empirical verification and analysis of latent representations' distribution and conditional output.
result Deep layers in neural networks can infer uncertainty similar to more computationally expensive methods.
UACQR improves CQR by separating aleatoric and epistemic uncertainties.
problem Ineffective CQR for problems with varying quantile regressor performance.
method Integrates aleatoric and epistemic uncertainties in CQR.
result UACQR provides stronger conditional coverage in simulated and real-world data.
Risk Advisor predicts and mitigates ML deployment failures.
problem Predicting and mitigating test-time failure risks of ML systems.
method Post-hoc meta-learner for estimating failure risks and uncertainties.
result Reliably predicts deployment-time failure risks across various ML models.
This work introduces a method to decompose uncertainty in in-context learning for large language models.
problem Understanding the sources of uncertainty in in-context learning for large language models.
method Variational uncertainty decomposition framework without sampling from latent parameter posterior.
result Quantitative and qualitative validation of decomposed epistemic and aleatoric uncertainties.
Estimates uncertainty for deep learning models without complex methods.
problem Uncertainty estimation for deep learning models.
method Simultaneous Quantile Regression (SQR) for aleatoric uncertainty and Orthonormal Certificates (OCs) for epistemic uncertainty.
result Estimates of aleatoric and epistemic uncertainty without ensembling or retraining deep models.
Study decomposes uncertainty in HK-distribution parameter estimation for QUS.
problem Uncertainty in HK-distribution parameter estimation for quantitative ultrasound.
method Bayesian Neural Networks (BNNs) for parameter estimation and uncertainty decomposition.
result Decomposes total predictive uncertainty into epistemic and aleatoric components.
New method improves estimation of neural network aleatoric uncertainty.
problem Existing methods overestimate aleatoric uncertainty in neural networks.
method Proposes a new de-noising method to estimate data uncertainty more accurately.
result Demonstrates better approximation of actual data uncertainty.
This paper examines sources of uncertainty in machine learning from a statistical perspective.
problem Quantifying uncertainty in supervised machine learning models.
method A conceptual, basic science approach examining aleatoric and epistemic uncertainty.
result Sources of uncertainty are diverse and cannot always be decomposed into aleatoric and epistemic.
DEUA detects diffusion-generated images by accounting for different types of uncertainty.
problem Detecting generated images with varying aleatoric and epistemic uncertainty.
method DEUA framework using Laplace approximation for DEU estimation and asymmetric loss function.
result DEUA achieves state-of-the-art performance on large-scale benchmarks.
VGE provides a practical approach to uncertainty estimation in ensemble models.
problem Uncertainty estimation in ensemble models using additive decomposition breaks down.
method Variance-Gated Ensembles (VGE) introduces a differentiable framework with a signal-to-noise gate.
result VGE provides a Variance-Gated Margin Uncertainty (VGMU) score and Variance-Gated Normalization (VGN) layer.
New acquisition function improves batch Bayesian active learning.
problem BatchBALD conflates epistemic and aleatoric uncertainty, leading to suboptimal performance.
method Focus on predictive probabilities to separate epistemic uncertainty, leading to better performance and faster evaluation.
result The new acquisition function performs better and allows for larger batches.
Unified Uncertainty Calibration improves AI predictions by combining different types of uncertainty.
problem AI classifiers struggle with uncertainty, leading to miscalibrated predictions and poor performance.
method Unified Uncertainty Calibration (U2C) combines aleatoric and epistemic uncertainties to improve prediction quality.
result U2C outperforms traditional reject-or-classify methods across various ImageNet benchmarks.
Enhances reinforcement learning uncertainty estimation with a generalized Gaussian error model.
problem Inaccurate error representations and compromised uncertainty estimation in conventional uncertainty-aware TD learning.
method Introduces a novel framework for generalized Gaussian error modeling in deep reinforcement learning, incorporating higher-order moments, particularly kurtosis, to improve uncertainty estimation and mitigation.
result Significant performance gains in policy gradient algorithms with the proposed framework.
The study quantifies uncertainty to improve model calibration and disambiguate annotator and data bias in emotion recognition.
problem Improving model interpretability and disambiguating bias in complex tasks like emotion recognition.
method Used a modified Monte Carlo dropout approach to quantify epistemic and aleatoric uncertainty.
result Identified a significant correlation between aleatoric uncertainty and human annotator disagreement.
Proposes measures for uncertainty quantification using proper scoring rules.
problem Uncertainty quantification for prediction tasks.
method Decomposes proper scoring rules into divergence and entropy components, tailoring uncertainty quantification to specific tasks.
result Flexibility in uncertainty quantification improves performance in selective prediction and active learning.
New method isolates epistemic uncertainty in diffusion models, improving plausibility scores.
problem Uncertainty quantification in diffusion models, especially epistemic uncertainty.
method Fisher information based approach using FLARE (Fisher-Laplace Randomized Estimator).
result FLARE improves uncertainty estimation in synthetic time-series generation tasks.