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…
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48 results for “statistical uncertainty”
The paper evaluates and improves uncertainty estimates in neural networks for safety-critical applications.
problem Quantifying uncertainty in neural networks for safety-critical systems.
method Proposes a statistical test for evaluating uncertainty realism in neural networks and transfers a classification architecture to image-to-image tasks.
result The variational U-Net architecture significantly improves uncertainty realism in image-to-image tasks compared to a plain model.
Isotonic regression binning affects calibration statistics of machine learning models.
problem Isotonic regression binning introduces aleatoric uncertainty in calibration statistics.
method Calibration error statistics are recalibrated using isotonic regression, which produces stratified uncertainties.
result Stratified uncertainties lead to significant differences in bin-based calibration statistics.
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.
This paper quantifies uncertainty in Data Shapley using statistical inference.
problem Uncertainty in data valuation due to dynamic data distribution.
method Established relationship with U-statistics and quantified uncertainty using statistical inference.
result Confidence intervals for Data Shapley estimations are provided.
FNNs can be made more interpretable with statistical methods.
problem FNNs lack interpretability and are often used as black-box models.
method Supplement FNNs with statistical inference and covariate-effect visualizations.
result FNNs can be made more like traditional statistical models.
Uncertainty Quantification of Locally Nonlinear Dynamical Systems using Neural Networksphysics.comp-ph
Neural networks simplify uncertainty quantification of locally nonlinear systems.
problem Estimating statistics of responses in large-scale locally nonlinear dynamical systems.
method Decomposes response into nominal linear system and a neural network-estimated pseudoforce.
result Neural networks can efficiently estimate pseudoforce containing nonlinear and uncertain information.
The paper sets limits on the accuracy of macroeconomic forecasts based on statistical moments and trade volumes.
problem Uncertainty in predicting macroeconomic variables like prices and returns.
method Defines theoretical lower bounds of uncertainty and upper limits on forecast accuracy based on statistical moments and trade volumes.
result Accuracy of forecasts of probabilities of macroeconomic variables doesn't exceed Gaussian approximations.
Uncertainty quantification has been a core of the statistical machine learning, but its computational bottleneck has been a serious challenge for both Bayesians and frequentists. We propose a model-based framework in quantifying uncertainty, called predictive-matching Generative Parameter Sampler (GPS). This procedure …
Normalizing flows improve ptychography reconstruction quality and uncertainty quantification.
problem Challenges in ptychography due to large-scale nonlinear and non-convex inverse problems and photon statistics.
method Use of normalizing flows to model the posterior distribution and quantify reconstruction uncertainty.
result Normalizing flows enable better characterization and uncertainty quantification in ptychography reconstructions.
New statistical guarantee improves conformal predictors for small datasets.
problem Uncertainty quantification for small datasets in surrogate models.
method Proposed a new statistical guarantee for conformal predictors, converging to standard CP for large datasets.
result The new guarantee offers relevant information about coverage for small data sizes, improving applicability.
A textbook on statistical machine learning for astronomy.
problem Uncertainty quantification in astronomical data analysis.
method Bayesian inference and classical statistical methods.
result Unified framework connecting modern and traditional methods.
Statistical finite elements use Langevin dynamics to efficiently handle uncertainty quantification.
problem Uncertainty quantification in finite element models with observed data.
method Langevin dynamics, unadjusted Langevin algorithm (ULA), for sampling posterior distributions.
result ULA provides a scalable and efficient method for characterizing the posterior distribution of statFEM models.
Method quantifies uncertainties in complex MRF models.
problem Uncertainties in MRF predictions due to data, modeling, and approximations.
method Information-based uncertainty quantification using MRF graphical structure.
result Tight bounds on predictions for quantities of interest in MRFs.
Bayesian inference with sources of uncertainty: from confidence modelling to sparse estimationstat.ME
Bayesian framework for encoding uncertainty and inducing sparsity.
problem Handling uncertainty and inducing sparsity in statistical models.
method General Bayesian framework with explicit encoding of uncertainty and sparsity-inducing approach.
result Effective in linear and logistic regression, and Bayesian neural networks.
Method combines LD and Fermat Distance for neural network uncertainty.
problem Measuring uncertainty in neural network predictions.
method Statistical Depth (LD) combined with Fermat Distance.
result Effective uncertainty estimation without impacting original model performance.
Study shows heavy-tailed distributions affect reliability of machine learning calibration statistics.
problem Reliability of calibration statistics for machine learning regression tasks is affected by heavy-tailed uncertainty and error distributions.
method Examined two calibration error estimation methods (CE and ZMS) and found ZMS to be less sensitive to heavy-tailed distributions.
result Heavy-tailed distributions make MSE and MV unreliable, but ZMS remains a reliable approach.
Certainty in Uncertainty: Reasoning over Uncertain Knowledge Graphs with Statistical Guaranteesstat.ML
UnKGCP generates prediction intervals for uncertain knowledge graphs with statistical guarantees.
problem Lack of quantified predictive uncertainty in existing UnKGE methods.
method Proposes extsc{UnKGCP} framework using conformal prediction with a novel nonconformity measure.
result Sharp prediction intervals effectively capture predictive uncertainty in diverse UnKGE methods.
New method reduces uncertainty in deep neural networks with minimal computation.
problem Uncertainty in over-parameterized neural networks hinders reliability and statistical guarantees.
method Procedural-noise-correcting (PNC) predictor and resampling methods.
result Asymptotically exact-coverage confidence intervals constructed with minimal computation.
Researchers improve imputation of missing data using diffusion transformers, quantifying uncertainty.
problem Enhancing the quality of time-series data with missing values.
method Conditional diffusion transformers for imputation, with statistical sample complexity bounds and uncertainty quantification.
result Theoretical insights into the efficiency and accuracy of imputation, influenced by missing patterns.
Proposes a new measure to evaluate stability of statistical parameters under distributional shifts.
problem Difficulty in transferring knowledge across data sets due to distributional changes.
method Introduces a measure of instability quantifying sensitivity of statistical parameters to Kullback-Leibler divergence and directional shifts.
result The proposed measure can elucidate the type of shifts a parameter is sensitive to and improve estimation accuracy under shifted distributions.
In stochastic decision problems, one often wants to estimate the underlying probability measure statistically, and then to use this estimate as a basis for decisions. We shall consider how the uncertainty in this estimation can be explicitly and consistently incorporated in the valuation of decisions, using the theory …
New method improves active statistical inference by reducing noise.
problem Inaccurate uncertainty estimates in active sampling lead to noisy results.
method Robust sampling strategies that interpolate between uniform and active sampling based on uncertainty scores.
result The robust sampling ensures that the estimator is never worse than uniform sampling and usually outperforms active inference.
Bayesian Neural Networks help quantify uncertainty in deep learning predictions.
problem Uncertainty quantification in deep learning predictions.
method Bayesian statistics applied to neural networks.
result Design, implementation, training, and evaluation of Bayesian Neural Networks.
We use statistical learning methods to construct an adaptive state estimator for nonlinear stochastic systems. Optimal state estimation, in the form of a Kalman filter, requires knowledge of the system's process and measurement uncertainty. We propose that these uncertainties can be estimated from (conditioned on) past…
Simple method improves uncertainty estimation for distribution shifts.
problem Improving uncertainty estimation in deep image classification under distribution shifts.
method Exposing original model to corrupted images and performing simple statistical calibration.
result Superior performance on various distribution shifts and unsupervised domain adaptation tasks.
Early stopping methods reduce unnecessary reasoning steps in LLMs by monitoring uncertainty signals.
problem LLMs sometimes generate unnecessary reasoning steps, especially under uncertainty.
method Statistically principled early stopping methods that monitor uncertainty signals during generation.
result Uncertainty-aware early stopping improves efficiency and reliability in LLM reasoning, especially in math reasoning.
This article describes a multivariate polynomial regression method where the uncertainty of the input parameters are approximated with Gaussian distributions, derived from the central limit theorem for large weighted sums, directly from the training sample. The estimated uncertainties can be propagated into the optimal…
The paper outlines future work in random sets theory.
problem Developing a theory of statistical reasoning with random sets.
method Generalizing logistic regression, probability laws, and geometric uncertainty.
result A new geometric approach to uncertainty with general random sets.
Study trade-offs between statistical and computational efficiency in variational inference.
problem Optimizing statistical accuracy vs. computational efficiency in Bayesian inference.
method Case study on Gaussian inferential models with diagonal plus low-rank precision matrices, analyzing Bayesian posterior inference and frequentist uncertainty quantification errors.
result Lower-rank models reduce variance and accelerate convergence but increase posterior inference error.
New method improves uncertainty quantification in latent variable models.
problem Uncertainty quantification in latent variable models with SGLD-Gibbs.
method Statistical scaling limit theory for SGLD-Gibbs, proposing hyperparameter tuning.
result Explicit guidance on hyperparameter tuning for SGLD-Gibbs ensures meaningful uncertainty quantification.
Rigorous uncertainty quantification of probabilistic AI weather forecasts with conformal predictionphysics.ao-ph
Rigorous uncertainty quantification of probabilistic AI weather forecasts with conformal prediction
problem Calibrated uncertainty quantification in probabilistic weather forecasts
method Conformal prediction
result Calibrated uncertainty at no expense to other probabilistic metrics
Method tackles uncertainty in reward models for LLMs from heterogeneous human feedback.
problem Uncertainty in reward models for LLMs from heterogeneous human feedback.
method Heterogeneous preference framework and alternating gradient descent algorithm.
result Established theoretical guarantees for estimator convergence and asymptotic distribution.
Optimum-statistical collaboration improves black-box optimization efficiency.
problem Improving black-box optimization efficiency through better statistical collaboration.
method Introducing optimum-statistical collaboration framework for hierarchical bandits-based optimization.
result Demonstrated improved regret bounds and better performance in experiments.
Combines deep and statistical learning for structured data.
problem Structured high-dimensional data challenges.
method Generates nonlinear features via sparse regularization and stochastic optimisation, uses probabilistic output layer for uncertainty.
result Achieves best of scalability and uncertainty quantification.
Deep RL evaluation underestimates uncertainty, leading to misleading conclusions.
problem Statistical uncertainty in deep RL performance evaluations is underestimated, leading to misleading conclusions.
method Advocates for reporting interval estimates of aggregate performance and proposes performance profiles to account for variability.
result Substantial discrepancies in prior performance comparisons are revealed, highlighting the need for more rigorous evaluation methods.
The CLT fails for LLM evaluations with small data, leading to underestimation of uncertainty.
problem Inaccurate uncertainty estimates in LLM evaluations with small datasets.
method Alternative frequentist and Bayesian methods for uncertainty quantification.
result CLT-based methods underestimate uncertainty in small data settings.
Efficient method for tensor linear form inference with noisy incomplete data.
problem Statistical inference of tensor linear forms with incomplete and noisy observations.
method Initial estimate + debiasing + one-step power iteration.
result Optimal uncertainty quantification and statistical-to-computational gaps examined.
Proposes -table for statistical SHAP explanations in regression models.
problem Lack of clear directional summaries, uncertainty, and fidelity in SHAP feature importance.
method SHAP importance selection, fitting a standardized linear surrogate, reporting coefficients, uncertainty, fidelity, and stability.
result Extends SHAP into a statistical global explanation with direction, uncertainty, fidelity, and stability.
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.
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.
In binary classification problems, mainly two approaches have been proposed; one is loss function approach and the other is uncertainty set approach. The loss function approach is applied to major learning algorithms such as support vector machine (SVM) and boosting methods. The loss function represents the penalty of …
Bayesian Neural Networksstat.ML
Bayesian neural networks help quantify prediction uncertainties in neural models.
problem Uncertainty in neural network predictions due to model randomness and lack of knowledge.
method Bayesian statistical framework to categorize uncertainty.
result Errors in neural network predictions can be obtained and characterized.
Validation of ML-UQ calibration statistics using simulated reference values: a sensitivity analysisstat.ML
Study validates ML-UQ calibration statistics using simulated reference values.
problem Validation of ML-UQ calibration statistics is lacking due to lack of predefined reference values.
method Proposed validation workflow using simulated reference values derived from synthetic datasets.
result Some statistics, like CC and ENCE, are overly sensitive to generative distribution choice.
New methods for uncertainty in neural networks with leaky ReLU activations.
problem Uncertainty in feed-forward neural networks with random input perturbations.
method Analytical expressions for PDF and moments of neural network output, linearization of leaky ReLU, Gaussian copula surrogate models.
result Accurate statistical results for large input perturbations, excellent agreement with Monte Carlo simulations.
USNRT uses tree-structured learning to improve uncertainty quantification of variance networks.
problem Improving uncertainty quantification of variance networks.
method Tree-structured local neural network model that partitions feature space into regions for training region-specific neural networks to predict mean and variance.
result USNRT shows superior performance in estimating uncertainty with variances on UCI datasets compared to recent methods.
Characterizes uncertainty in high-dimensional linear classification models.
problem Assessing uncertainty in high-dimensional linear classification models.
method Approximate message passing algorithm for posterior marginals, closed-form formula for joint statistics.
result Closed-form formula for joint statistics between logistic classifier, Bayesian uncertainty, and ground-truth probit uncertainty.
Bayesian approach quantifies uncertainty in LLM evaluations.
problem Statistical uncertainty in evaluating LLM behavior.
method Bayesian evaluation of LLM behavior using probabilistic text generation strategies.
result Bayesian approach provides useful uncertainty quantification about LLM behavior.