Bayesian UQ matches frequentist UQ for adaptively collected data.
problem Uncertainty quantification for adaptive data collection.
method Extends Bernstein-von Mises theorem to adaptively collected data.
result Bayesian UQ asymptotically matches Wald-type frequentist UQ.
Global sensitivity analysis improves BNN hyperparameter selection for accurate uncertainty quantification.
problem Difficulties in obtaining accurate uncertainty quantification with Bayesian Neural Networks (BNNs).
method Global sensitivity analysis of BNN performance under varying hyperparameter settings.
result Many hyperparameters interact to affect both predictive accuracy and uncertainty quantification.
OOD-trained Bayesian neural networks perform similarly to frequentist methods in uncertainty quantification.
problem Bayesian neural networks struggle in out-of-distribution (OOD) detection tasks.
method Incorporated out-of-distribution data into Bayesian inference through four different methods.
result OOD-trained Bayesian neural networks are competitive with frequentist baselines.
New algorithm speeds up Bayesian UQ for high-dimensional inverse problems.
problem Computational inefficiency in Bayesian inference for high-dimensional inverse problems.
method Deep neural network-based autoencoder for dimension reduction and emulation phase.
result Computational efficiency up to three orders of magnitude with scalable Bayesian UQ.
Improves inverse uncertainty quantification for time-dependent data using PCA and deep neural networks.
problem Efficiently quantify model input uncertainties from time-dependent experimental data.
method Functional PCA for dimensionality reduction, deep neural networks for surrogate modeling, Bayesian neural networks for uncertainty estimation.
result The proposed method reduces the computational cost and improves the agreement with experimental data.
Bayesian adaptive PCE method improves surrogate modeling and sensitivity analysis.
problem Lack of fully Bayesian PCE methods in statistics.
method Developed a novel fully Bayesian adaptive PCE method with R implementation.
result Bayesian adaptive PCE provides competitive performance for various UQ tasks.
Study evaluates machine learning methods for uncertainty quantification in complex systems.
problem Accurately quantify epistemic and aleatoric uncertainties in complex dynamical systems.
method Examined Gaussian processes, UQ-augmented neural networks (ENN, BNN, D-NN, G-NN) on two model data sets.
result Concluded on model architecture and hyperparameter tuning for improved UQ accuracy.
This paper surveys UQ methods for deep learning.
problem Overconfident predictions in deep learning models.
method Categorizes UQ methods by uncertainty sources.
result Identifies strengths and limitations of each category.
Last-layer approximation improves UQ performance without sacrificing computational efficiency.
problem Epistemic uncertainty quantification for deep neural networks.
method Comparison of full-network and last-layer linearization using theoretical and empirical approaches.
result Last-layer approximation yields comparable UQ performance with improved computational efficiency.
GABI learns geometry from diverse systems to improve Bayesian inference.
problem Bayesian inversion of physical systems with varying geometries.
method Geometric Autoencoders for Bayesian Inversion (GABI) learns geometry-aware priors from large datasets.
result GABI yields comparable predictive accuracy to deterministic methods and well-calibrated uncertainty quantification.
Comparison of UQ methods in deep learning for a simple physical system.
problem Uncertainty quantification in deep learning for physical systems.
method Bayesian Neural Networks (BNN), Concrete Dropout (CD), Deep Ensembles (DE), and Analytic Error Propagation.
result Pitfalls in using UQ methods, especially Bayesian Neural Networks and Concrete Dropout.
JaxSGMC simplifies SG-MCMC for Bayesian deep learning.
problem Uncertainty quantification in deep learning models.
method Modular stochastic gradient MCMC in JAX.
result Facilitates trustworthy neural network predictions.
New method for neural network uncertainty quantification using empirical Neural Tangent Kernel.
problem Accurately quantify uncertainty in neural network predictions.
method Post-hoc, sampling-based approach using gradient-descent on linearized networks.
result Method effectively approximates Gaussian process posterior and outperforms existing methods in efficiency and accuracy.
Study evaluates quality of uncertainty estimates for neural networks.
problem Lack of principled assessment methods for evaluating uncertainty quality in deep learning.
method Statistical methods of frequentist interval coverage, interval width, and expected calibration error.
result Different UQ methods produce markedly different quality uncertainty estimates.
This paper studies uncertainty quantification in deep spatiotemporal forecasting.
problem Uncertainty quantification in deep spatiotemporal forecasting models.
method Analysis of UQ methods from Bayesian and frequentist perspectives, including statistical decision theory.
result Different UQ methods have different strengths and weaknesses, with Bayesian methods being more robust in mean prediction and frequentist methods providing more extensive coverage.
BODE enhances deep neural network predictions and uncertainty quantification in safety modeling.
problem Uncertainty in deep neural network predictions for safety-critical applications.
method Bayesian optimization combined with deep ensembles (BODE).
result BODE reduces total uncertainty by over 30% compared to a manually tuned baseline ensemble.
SBMC method improves uncertainty estimation in deep learning models.
problem Improving uncertainty quantification in deep learning models.
method A scalable Bayesian Monte Carlo method using a model and parallel SMC/MCMC algorithm.
result SBMC achieves comparable or better accuracy and improved uncertainty quantification compared to state-of-the-art methods.
New methods improve uncertainty quantification in dynamic biological systems.
problem Uncertainty in dynamic biological models due to nonlinearity and parameter sensitivity.
method Conformal inference methods for non-asymptotic guarantees.
result Enhanced robustness and scalability for diverse biological data structures.
CoT-UQ improves LLM uncertainty quantification by integrating reasoning steps.
problem LLMs' overconfidence and lack of response-wise uncertainty quantification.
method Integrates LLMs' reasoning steps into uncertainty estimation.
result Significantly improves uncertainty quantification accuracy (5.9% AUROC improvement).
Deep Learning (DL) methods have been transforming computer vision with innovative adaptations to other domains including climate change. For DL to pervade Science and Engineering (S&E) applications where risk management is a core component, well-characterized uncertainty estimates must accompany predictions. However, S…
BKTF uses tensor factorization for Bayesian optimization of complex functions.
problem Complex functions with nonstationary, nonseparable, and multimodal features.
method Bayesian Kernelized Tensor Factorization (BKTF) approximates complex functions using a low-rank tensor CP decomposition with GP priors.
result BKTF provides flexible and effective surrogate modeling with uncertainty quantification.
Paper introduces MCSD, a method for uncertainty estimation in deep learning.
problem Need for reliable uncertainty quantification in deep neural networks.
method Theoretical connection to variational inference and empirical benchmarking of MCSD.
result MCSD achieves competitive predictive accuracy and improves uncertainty ranking.
As neural networks have begun performing increasingly critical tasks for society, ranging from driving cars to identifying candidates for drug development, the value of their ability to perform uncertainty quantification (UQ) in their predictions has risen commensurately. Permanent dropout, a popular method for neural …
New ML methods improve physical system understanding by quantifying uncertainty across diverse regimes.
problem Capturing multi-regime physical systems with standard ML techniques.
method Coverage-oriented uncertainty quantification (UQ) methods.
result Coverage-oriented UQ models deliver physically consistent uncertainty estimates.
Unified Bayesian framework for uncertainty quantification in mechanics.
problem Propagation of input uncertainties and inference of unknown parameters.
method Bayesian probability theory for forward and inverse problems.
result Unified theoretical framework for both forward and inverse UQ.
Researchers use active subspaces to quantify uncertainty in deep generative models for molecular design.
problem Uncertainty quantification in deep generative models for molecular design due to high parameter space.
method Leveraging active subspaces to approximate posterior distribution over low-dimensional parameters.
result The proposed UQ scheme effectively estimates epistemic uncertainty in high-dimensional parameter space without altering model architecture.
PCS-UQ framework improves uncertainty quantification for machine learning models.
problem Ensuring trustworthy uncertainty quantification for machine learning models in high-stakes domains.
method PCS-UQ framework based on Predictability, Computability, and Stability principles, integrating prediction-checking, bootstrap samples, and multiplicative calibration.
result PCS-UQ maintains target coverage while outperforming or matching conformal methods in interval width and subgroup coverage.
A scalable GP model for online uncertainty quantification over graphs.
problem Scalable uncertainty quantification over graphs with dynamic data.
method Graph-aware parametric Gaussian process model using random features and online conformal prediction.
result Improved coverage and efficient prediction sets over existing methods.
New algorithm improves Gaussian process hyperparameter tuning for large datasets.
problem Scalable hyperparameter tuning for Gaussian processes on large datasets.
method Estimates smoothness and length-scale parameters in Matern kernel using novel loss functions.
result Improved uncertainty quantification over traditional methods.
ELUQuant quantifies uncertainties in DIS events using BNNs and MNFs.
problem Uncertainty quantification in Deep Inelastic Scattering (DIS) events.
method Physics-informed Bayesian Neural Network with flow approximated posteriors.
result Effective extraction of kinematic variables x, Q2, and y with detailed event-level uncertainty. This research formalizes uncertainty quantification for Universal Differential Equations models.
problem Quantifying uncertainties in Universal Differential Equations models.
method Formalized uncertainty quantification methods for UDEs, including frequentist and Bayesian approaches.
result Evaluation of ensemble, variational inference, and MCMC sampling methods for UDEs.
AI helps HEP measure uncertainties, but needs better interpretation.
problem Uncertainty interpretation in AI for HEP measurements.
method Discussing existing AI methods for inference, simulation, and control/decision-making.
result Need for trustworthy AI UQ methods for widespread usage.
New approach connects UQ in SciML to viscous HJ PDEs for efficient uncertainty quantification.
problem Challenges in interpretability and expensive training procedures in UQ for SciML.
method Established connection between Bayesian inference and viscous HJ PDEs, developed Riccati-based methodology.
result Efficiently updates model predictions without retraining or data access, suitable for real-time inferences.
New method calibrates LLMs for safety-critical tasks with scalable Bayesian inference.
problem Overconfidence in LLMs after fine-tuning for specific tasks.
method Orthogonalized Low-Rank Adapters (PoLAR) with variational Bayesian inference.
result Scalable and well-calibrated uncertainty estimation for LLMs.
Bayesian approach quantifies uncertainty in LLM-based systems.
problem Uncertainty quantification in LLM-based systems, especially for high-stakes applications.
method Interpreting prompts as parameters in a Bayesian model, using MHLP for inference.
result Improvements in predictive accuracy and uncertainty quantification on various benchmarks.
Bayesian methods detect and forecast inclinometer anomalies in UK rail data.
problem Detecting and predicting dangerous movements in earthwork slopes.
method Bayesian UQ techniques applied to latent Markov process and non-linear Bayesian filter.
result Anomaly detection and forecasting demonstrated on large real-world data.
This study revisits UQ validation methods based on consistency and adaptivity concepts.
problem Lack of comprehensive validation methods for UQ metrics across input feature ranges.
method Revisit and extend common validation methods for UQ metrics based on consistency and adaptivity concepts.
result Improved understanding and capabilities of UQ metrics validation methods.
This paper is concerned with a lesser-studied problem in the context of model-based, uncertainty quantification (UQ), that of optimization/design/control under uncertainty. The solution of such problems is hindered not only by the usual difficulties encountered in UQ tasks (e.g. the high computational cost of each forw…
A new method combines SciML and UQ with physical constraints.
problem Uncertainty quantification in scientific machine learning tasks.
method Physics-constrained polynomial chaos expansion.
result Effective uncertainty quantification and SciML integration.
SLUG method detects bias and out-of-distribution content in generative models.
problem Generative models can underrepresent certain groups and fail on out-of-distribution data.
method SLUG: A new uncertainty quantification method for VAEs combining Laplace approximations and stochastic trace estimators.
result SLUG's UQ score correlates with bias and out-of-distribution content.
Proposes a simpler method for quantifying uncertainty in time-series with volatility clustering.
problem Uncertainty quantification for time-series with volatility clustering.
method Proposes a Scale Mixture Distribution to quantify return forecast uncertainty in neural networks.
result The proposed method provides a favorable complexity-accuracy trade-off and separates model parameters into subnetworks.
A decision-theoretic bootstrapping method for robust uncertainty quantification.
problem Uncertainty in finite data sets and distributional shift between training and testing data.
method Partition data, train models, sample UQ subsets, define adversarial game, identify optimal mixed strategies.
result Optimal model mixtures and UQ estimates for robust uncertainty quantification.
Beam search improves UQ in LLMs by reducing duplicates and variance.
problem Peaked distributions in multinomial sampling lead to duplicates and high variance in uncertainty estimates.
method Employ beam search to generate candidates for consistency-based UQ, providing a theoretical lower bound and empirical evaluation.
result Beam search achieves smaller error than multinomial sampling, leading to state-of-the-art UQ performance.
DVE uses GPs on DNN outputs to provide UQ without retraining.
problem Feature collapse in DNNs affects UQ methods.
method Deep Vecchia ensemble (DVE) of GPs on DNN hidden layers.
result Deterministic UQ possible in feature-collapsed DNNs.
This research improves model interpretability and uncertainty estimation for deep learning models on non-iid data.
problem Improving interpretability and uncertainty estimation for deep learning models on non-iid data.
method 4 UQ approaches (BNN, SWAG, MC dropout, ensemble) applied to ARMED MEDL models.
result Ensemble approaches, especially with 90% subsampling, provide best performance in prediction and uncertainty estimation.
Motivation: Ab initio protein docking represents a major challenge for optimizing a noisy and costly "black box"-like function in a high-dimensional space. Despite progress in this field, there is no docking method available for rigorous uncertainty quantification (UQ) of its solution quality (e.g. interface RMSD or iR…
Flexible evidential deep learning improves uncertainty quantification in machine learning.
problem Overconfident predictions in machine learning models can lead to serious consequences.
method Proposes flexible evidential deep learning (F-EDL) to model uncertainty over class probabilities using a flexible Dirichlet distribution.
result Empirically demonstrates state-of-the-art uncertainty quantification performance across diverse scenarios.
MC-CP combines adaptive MC dropout with conformal prediction for robust uncertainty quantification.
problem Deploying deep learning models in safety-critical applications requires reliable confidence estimates.
method MC-CP integrates adaptive Monte Carlo dropout with conformal prediction to improve model performance.
result MC-CP significantly outperforms state-of-the-art UQ methods in both classification and regression tasks.