New method explains sensitivity of test data uncertainty in Bayesian inference.
problem Widespread belief that test data similarity reduces epistemic uncertainty.
method Information-theoretic decomposition of predictive uncertainty.
result Defines sensitivity using information-theoretic quantities.
Qualitative analysis of MC dropout for NN model uncertainty.
problem Measuring uncertainty in neural network models.
method Mathematical formulation of Monte Carlo dropout and its benefits/costs in NN models.
result Potential benefits and associated costs of using MC dropout in NN models.
This paper analyzes uncertainty in DFN simulations using sensitivity analysis.
problem Uncertainty in estimating QoI due to epistemic and aleatoric uncertainties in DFN simulations.
method Sensitivity analysis to attribute uncertainty to input parameters and aleatoric uncertainty.
result Characterizes uncertainty in DFN flow simulations with heteroskedastic aleatoric uncertainty.
VarFA efficiently estimates student skill levels with uncertainty for adaptive testing.
problem Efficiently estimating student skill levels with uncertainty for adaptive testing.
method VarFA uses variational inference to extend factor analysis models for educational data.
result VarFA efficiently handles large datasets and produces uncertainty estimates.
This paper uses robust optimization to analyze supply chain resilience.
problem Supply chain resilience analysis of multi-modal logistics networks.
method Robust optimization with budget-of-uncertainty.
result Interactive effects of network size, disruption scale, and degree on resilience.
Persistence norms explain financial uncertainty better than volatility.
problem Capturing financial instability and predictability.
method Applied topological data analysis to financial markets.
result Persistence norms are significant in explaining financial uncertainty, while volatility is less effective.
A novel double-space tensor-product RKHS framework for hybrid uncertainty sensitivity analysis.
problem Quantifying the influence of hybrid aleatory and epistemic uncertainties on high-dimensional system responses.
method A novel double-space tensor-product RKHS framework for sensitivity analysis under hybrid uncertainty.
result Concurrent double Möbius inversion orthogonally decomposes global dependence measure into pure aleatory effects, pure epistemic effects, and their interaction contributions.
Study improves LLMs for PPI analysis by addressing uncertainty.
problem Uncertainty in LLM predictions for PPIs.
method Fine-tuned LLaMA-3 and BioMedGPT models, LoRA ensembles, Bayesian LoRA for UQ.
result Competitive PPI identification performance across diverse disease contexts.
Statistical uncertainty of different filtration techniques for market network analysis is studied. Two measures of statistical uncertainty are discussed. One is based on conditional risk for multiple decision statistical procedures and another one is based on average fraction of errors. It is shown that for some import…
DeepONet accelerates reliability analysis of stochastic nonlinear systems.
problem Time-dependent reliability analysis of systems with stochastic forcing.
method DeepONet, a novel operator network, learns function-to-function mappings.
result DeepONet efficiently and accurately predicts system responses.
This research assesses uncertainty quantification and sensitivity analysis for DTs in nuclear fuel performance.
problem Understanding the reliability and performance of advanced nuclear fuels using DTs.
method Introduces ML-based uncertainty quantification and sensitivity analysis methods applied to BISON fuel performance code.
result Demonstrates the effectiveness of DTs in multi-criteria decision-making for nuclear fuel performance.
Paper uses PCE to quantify ML model and input uncertainties.
problem Accurately quantify and propagate combined uncertainties in ML predictions.
method Polynomial Chaos Expansion (PCE) for joint input and model uncertainty.
result Efficient and accurate calculation of output variability and sensitivity.
We derive a novel sensitivity analysis of input variables for predictive epistemic and aleatoric uncertainty. We use Bayesian neural networks with latent variables as a model class and illustrate the usefulness of our sensitivity analysis on real-world datasets. Our method increases the interpretability of complex blac…
Study finds multifractal cross-correlations between agricultural markets and external uncertainties.
problem Investigating relationships between agricultural spot markets and external uncertainties.
method Multifractal detrending moving-average cross-correlation analysis (MF-X-DMA).
result Maize exhibits intrinsic joint multifractality with all uncertainty proxies.
We present a technique to perform dimensionality reduction on data that is subject to uncertainty. Our method is a generalization of traditional principal component analysis (PCA) to multivariate probability distributions. In comparison to non-linear methods, linear dimensionality reduction techniques have the advantag…
Techniques for understanding the functioning of complex machine learning models are becoming increasingly popular, not only to improve the validation process, but also to extract new insights about the data via exploratory analysis. Though a large class of such tools currently exists, most assume that predictions are p…
Bayesian CNN improves MRI stroke diagnosis accuracy and uncertainty quantification.
problem Uncertainty quantification in automated image analysis for medical decision-making.
method Bayesian Convolutional Neural Network (CNN) with aggregation methods for patient-level diagnoses.
result Bayesian CNN achieved 95.33% accuracy on 511 patients, 2% higher than non-Bayesian.
Study finds market inefficiencies vary by time scale, with news uncertainty key.
problem Evaluating scale-dependent informational efficiency of stock markets.
method Tensor-eigenvalue-based Financial Chaos Index, Granger causality, network analysis.
result Semi-strong form of EMH rejected at daily frequency, but not at monthly.
The paper tackles mean-variance analysis in Bayesian optimization under uncertainty.
problem Optimizing decisions in uncertain environments considering trade-offs between average and variance of risk.
method Developed bounds for mean and variance risk measures in Gaussian Process models and proposed AL algorithms for multi-task, multi-objective, and constrained optimization scenarios.
result Proposed AL algorithms effectively address the mean-variance trade-off in uncertain optimization scenarios.
HybridCGAN improves portfolio analysis by balancing trend prediction and market uncertainty.
problem Markowitz framework's overemphasis on market uncertainty and trend prediction.
method A hybrid approach combining deep generative models to balance trend prediction and market uncertainty.
result HybridCGAN leads to better portfolio allocation compared to existing methods.
NeuralSurv models survival analysis with Bayesian uncertainty.
problem Capturing time-varying risk relationships in survival analysis.
method Two-stage data-augmentation scheme, mean-field variational algorithm, coordinate-ascent updates, locally linearized Bayesian neural network.
result Delivers superior calibration compared to state-of-the-art models.
New model predicts financial market abnormalities using stock index uncertainties.
problem Forecasting abnormal financial fluctuations in the market.
method Quantitative analysis of mean and volatility uncertainties, constructing early warning indicators.
result Established a new abnormal fluctuations warning model.
Paper extends quantile factor analysis with probabilistic methods for better economic policy and financial condition prediction.
problem Improving accuracy in economic and financial condition prediction.
method Probabilistic quantile factor analysis with regularization and variational approximations.
result The probabilistic estimator outperforms a recent loss-based estimator in many cases.
Proposes a sample-efficient method for uncertainty estimation in deep learning.
problem Inaccurate uncertainty estimation in deep learning models, especially with limited data.
method Probabilistic Neighbourhood Component Analysis (PCA) for sample-efficient uncertainty estimation.
result Demonstrates superior uncertainty quantification compared to state-of-the-art methods.
The paper addresses data uncertainty in graph embedding by modeling data points as Gaussian distributions.
problem Data uncertainty in machine learning pipelines leads to misleading embeddings and lower accuracy.
method The paper proposes modeling data uncertainty using Gaussian distributions and reformulates graph embedding techniques.
result The proposed methods improve the accuracy of graph embedding by accounting for data uncertainty.
The paper quantifies and attributes uncertainty in complex system simulations.
problem Uncertainty in complex system simulations due to unknown or approximated subprocesses.
method Developed a framework for quantifying and attributing submodel uncertainty using bootstrapping, Bayesian model averaging, and tree-based methods.
result Individual submodels contribute to overall uncertainty, and their importance can be quantified.
SBI improves uncertainty analysis of cardiovascular biomarkers.
problem Mapping waveforms back to plausible physiological parameters.
method Simulation-based inference (SBI) for statistical inference.
result Posterior distributions provide a multi-dimensional representation of uncertainty.
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.
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.
The paper tackles inverse uncertainty quantification in neutron noise analysis.
problem Uncertainty in estimating material properties from noisy neutron correlation measurements.
method Surrogate models and inverse uncertainty quantification to account for measurement error and model bias.
result Improved prediction of neutron correlations and quantification of uncertainties.
NP-ODE models FEA simulations with uncertainty, improving accuracy and efficiency.
problem Limitations of FEA in terms of computational cost and uncertainty quantification.
method Physics-informed neural process aided ordinary differential equations (NP-ODE).
result NP-ODE outperforms benchmark methods in uncertainty quantification and prediction accuracy.
This paper tackles reliability analysis for stochastic systems using surrogate models.
problem Traditional reliability analysis relies on deterministic models, which are not suitable for stochastic systems with non-repeatable outcomes.
method The paper introduces reliability analysis for stochastic models by using generalized lambda models and stochastic polynomial chaos expansions as surrogate models to lower computational cost.
result The surrogate models enable efficient uncertainty quantification at a lower cost than traditional Monte Carlo simulation.
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.
Uncertainty principles such as Heisenberg's provide limits on the time-frequency concentration of a signal, and constitute an important theoretical tool for designing and evaluating linear signal transforms. Generalizations of such principles to the graph setting can inform dictionary design for graph signals, lead to …
Review of uncertainty representation methods in risk management.
problem Inadequate consideration of uncertainty in risk management.
method Systematic literature review of 370 publications.
result Probabilistic methods are predominant, but fuzzy and evidence-based approaches are also useful.
Large models collapse epistemic uncertainty, challenging traditional wisdom.
problem Epistemic uncertainty collapse in large models.
method Implicit ensembling and decomposition techniques.
result Larger models can collapse epistemic uncertainty, contrary to expectations.
Surveying risk measures for handling uncertainty in various fields.
problem Handling uncertainty in engineering and data-driven problems.
method Review of risk measures and their applications.
result Rapid development and widespread use of risk measures.
The paper proposes a new method for modeling and quantifying uncertainty in multiple closed curves.
problem Modeling and uncertainty quantification of multiple closed curves.
method A multiple-output, multi-dimensional Gaussian process modeling framework.
result The proposed method provides meaningful uncertainty quantification for curve and shape-related tasks.
The investor is interested in the expected return and he is also concerned about the risk and the uncertainty assumed by the investment. One of the most popular concepts used to measure the risk and the uncertainty is the variance and/or the standard-deviation. In this paper we explore the following issues: Is the stan…
PAGER detects failures in deep regression models using a new framework.
problem Detecting failures in deep regression models.
method PAGER uses a combination of epistemic uncertainty and manifold non-conformity scores.
result PAGER accurately characterizes and detects failures in deep regressors.
Method quantifies sensitivity of reliability analysis to uncertainty sources.
problem Computational expense in reliability analysis of complex models.
method Gaussian process surrogate model, active learning, sensitivity analysis.
result Reduces main source of error in estimating rare event probabilities.
ConfEviSurrogate improves surrogate model accuracy and uncertainty quantification.
problem Uncertainty in surrogate models hinders reliable analysis.
method Introduces ConfEviSurrogate, a novel model that learns evidential distributions, separates uncertainty sources, and provides reliable prediction intervals.
result Demonstrates accurate predictions and robust uncertainty estimates in various simulations.
SAWAR improves SA models by making them more robust to data uncertainties.
problem Improving survival analysis models' robustness to data uncertainties.
method Adversarial robustness through Min-Max optimization with CROWN-IBP.
result SAWAR outperforms baseline methods and SOTA models across various metrics.
Analyzes Indian chemical industry post-Covid.
problem Global uncertainty impacts chemical industry performance.
method Fundamental analysis of key players and trends.
result Various geopolitical and macroeconomic trends shape industry performance.
Robust optimization improves portfolio selection by accounting for deep uncertainties.
problem Managing deep uncertainties in future asset returns for successful portfolio selection.
method Robust optimization (RO) models incorporating general assumptions on uncertain risk parameters.
result RO models outperform traditional models in comprehensive empirical assessments.
Study benchmarks uncertainty quantification in chest X-ray classification.
problem Reliable uncertainty quantification for medical AI models.
method Evaluation of 13 uncertainty quantification methods on MIMIC-CXR-JPG dataset.
result Insights into effectiveness and disentanglement of epistemic and aleatoric uncertainties.
Extends fractional Lp uncertainty principles with extremizers and stability results.
problem Investigating uncertainty principles in fractional Lp settings. method Analyzing the fractional Schrödinger equation to find extremal functions and sharp constants.
result Proves stability of extremizers for fractional uncertainty inequalities.
Hybrid model integrates GATv2 and geostatistics for better spatial prediction and uncertainty.
problem Accurate spatial prediction and uncertainty quantification in epidemiology and risk analysis.
method Integrates Graph Attention Network (GATv2) with model-based geostatistics (MBG) to capture relational and spatial dependencies.
result Hybrid model improves predictive accuracy and uncertainty quantification compared to standalone models.