Paper tackles edge computing for reliable ITS data analytics.
problem Real-time data analytics for ITSs with low latency and reliability.
method Edge-centric architecture with deep learning for heterogeneous data.
result Preliminary results show reliable, secure ITS environment.
k-Rater reliability corrects under-reporting of aggregated data reliability.
problem Under-reporting of data reliability in aggregated ratings.
method k-Rater reliability (kRR) as a multi-rater generalization of IRR.
result kRR provides a more accurate measure of reliability for aggregated datasets.
Survey examines big data's role in network design.
problem Designing robust networks with refined performance and intelligent features.
method Integrates big data analytics with network control/traffic layers.
result Big data analytics can improve network design.
Study defines and optimizes bank reliability using LR and PSO.
problem Lack of reliability concept in financial services.
method Logistic Regression (LR) for initial estimation, Particle Swarm Optimization (PSO) for optimization.
result Optimal financial ratios maximize bank reliability.
Paper introduces a cost function for balancing accuracy and reliability in probabilistic forecasts.
problem Assigning uncertainties to single-point predictions in probabilistic forecasts.
method Introduces an Accuracy-Reliability cost function and derives analytic formula for Gaussian distributions. Employs a two-objective optimization problem to estimate variance in heteroskedastic regression.
result The method can accurately estimate variance in heteroskedastic regression problems, as shown in synthetic data examples.
CovidSens uses social media to track COVID-19 spread.
problem Accurate and timely dissemination of COVID-19 information.
method Social sensing to analyze online user data.
result Real-time COVID-19 spread tracking system.
Smooth KANs improve model reliability in computational biomedicine.
problem Limited convergence of KANs in representing generic smooth functions.
method Introducing smooth, structurally informed KANs that can approximate MLPs in specific function classes.
result Smooth KANs can achieve equivalence to MLPs in specific function classes, enhancing model reliability and performance.
Proposes deep-RL with GANs for ultra-reliable low-latency communication.
problem Resource allocation for URLLC with high reliability and low latency.
method Experienced deep-RL framework using GANs to pre-train and optimize resource allocation.
result Deep-RL framework achieves near-optimal reliability and latency under URLLC constraints.
A new method for non-rigid point set registration reduces computational complexity.
problem Efficiently registering non-rigid point sets with large numbers of points.
method Structured Analytic Coherent Point Drift (Analytic-CPD) reformulates CPD for structured analytic mappings.
result Analytic-CPD reduces computational complexity by controlling the deformation model's dimensionality.
mfEGRA uses active learning to efficiently locate failure boundaries in reliability analysis.
problem Prohibitive cost of reliability analysis using Monte Carlo sampling for high-fidelity models.
method Develops a multifidelity active learning method using data-driven adaptively refined surrogates.
result Significant computational savings (46-48%) compared to single-fidelity EGRA.
In this paper, we consider the joint design of data compression and 802.15.4-based medium access control (MAC) protocol for smartgrids with renewable energy. We study the setting where a number of nodes, each of which comprises electricity load and/or renewable sources, report periodically their injected powers to a da…
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.
Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks. New challenges arise in multilayer graph clustering for assigning clusters to a common multilayer node set and for combining information from each layer. This paper presents a theo…
New metrics improve reliability of image generation evaluation.
problem Lack of reliable metrics for evaluating fidelity and diversity in generative models.
method Proposed density and coverage metrics to diagnose fidelity and diversity separately.
result Density and coverage metrics provide more interpretable and reliable signals.
DeepSKA provides interpretable, reliable neural approximations for SRNs.
problem Estimating expected outputs in SRNs is computationally challenging and unreliable.
method Jointly achieves interpretability, reliability, and computational gains with DeepSKA.
result Delivers accurate predictions and substantial efficiency improvements across SRNs.
Synthesizes machine learning applications in reliability and safety.
problem Navigating the fragmented literature on ML for reliability and safety.
method Overview of ML categories, review of applications, discussion of Deep Learning.
result Machine learning can provide novel insights and improve accident prevention.
The paper presents a method to assign accurate and calibrated uncertainties to deterministic model predictions.
problem Assigning uncertainties to deterministic model predictions.
method Transforming deterministic predictions into probabilistic ones using a cost function that balances accuracy and reliability.
result The method improves the reliability of probabilistic predictions without sacrificing accuracy.
Neural networks combining multiple data sources can reverse preferences, affecting decision reliability.
problem Preference reversals in neural networks under pooled data.
method Formalized through Case-Based Decision Theory, analyzed Gram geometry, introduced regularization, and developed auditing methods.
result Pooled refitting can reverse shared preferences, and conditions for preserving preferences are derived.
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 inference framework for spatiotemporal data.
problem Challenges in extracting mechanistic insights from complex spatiotemporal data.
method Vision transformer-driven variational encoding and likelihood-free Bayesian approach.
result Unified inference framework for identifying spatial and temporal patterns.
A new method identifies critical transitions in high-dimensional data.
problem Challenges in identifying critical transitions in high-dimensional time-series data.
method Spatial-temporal Principal Component Analysis (stPCA)
result Identifies tipping points before critical transitions reliably.
A new score measures data reliability without ground truth.
problem Assessing reliability of datasets without access to ground truth.
method Define ground-truth-based orderings and propose Gram determinant score.
result Gram determinant score effectively captures data quality across diverse observation processes.
Study compares analytical and bootstrap DML confidence intervals across various machine learning algorithms.
problem Impact of machine learning algorithm choice on DML confidence intervals.
method Comprehensive simulation study comparing analytical and bootstrap DML confidence intervals across different machine learning algorithms.
result Substantial variability in coverage performance across analytical and bootstrap confidence intervals, highlighting the importance of learner choice.
New methods correct bias in LLM-as-a-Judge evaluations, but reliability depends on judge quality and model calibration.
problem Systematic bias in LLM-as-a-Judge evaluations using naive estimators.
method Analytical results, simulations, and real-data case study to diagnose reliability of corrected estimates.
result Corrected estimates, especially shared-calibration comparisons, can be unreliable under certain conditions.
Study evaluates machine learning methods for large-scale network reliability, revealing ANN's and PR's performance.
problem Tackles the NP-hard problem of approximating binary-state network reliability for large-scale systems.
method Compares 20 machine learning methods across three reliability regimes and evaluates their performance on large-scale networks.
result Large-scale networks with arc reliability ≥ 0.9 exhibit near-unity system reliability, enabling computational simplifications.
The paper studies how to allocate human validation in AI-assisted tasks to minimize errors.
problem Heterogeneous reliability of AI-generated signals across tasks, products, and customer segments.
method Tuned prediction-powered inference, upper confidence bounds policy, Neyman square-root rule.
result The proposed policy outperforms uniform and epsilon-greedy allocation, closing most of the gap to the oracle when reliability is heterogeneous.
A machine learning framework simulates complex multibody dynamics systems.
problem Simulating complex multibody dynamics systems accurately and efficiently.
method Employing deep neural networks to generate a data-driven meta-model of multibody systems.
result The meta-model accurately predicts motion data of multibody systems without solving equations of motion.
Enhanced metrics improve generative model evaluation reliability.
problem Lack of reliable quality metrics for generative models.
method Introduce Clipped Density and Clipped Coverage metrics.
result Metrics prevent out-of-distribution samples from biasing quality scores.
Paper establishes a formula linking model performance to insurance loss ratio.
problem Improving model performance does not always lead to proportional improvements in loss ratio.
method Derives a closed-form formula connecting Pearson correlation to expected loss ratio.
result Model improvements have diminishing marginal returns in reducing loss ratio.
Improves neural network performance by dynamically adjusting model weights based on source reliability.
problem Training neural networks on data from unreliable sources leads to poor performance.
method Dynamic re-weighting strategy using likelihood tempering to adjust model weights based on estimated source reliability.
result Significant improvement in model performance when trained on mixtures of reliable and unreliable data sources.
Paper presents a method to accurately quantify neural network uncertainty without sampling.
problem Uncertainty quantification in neural networks for reliability and robustness.
method Sample-free moment propagation technique for mean vectors and covariance matrices.
result Analytic solution for covariance of nonlinear activation functions.
Paper explores physics-informed deep learning for system reliability assessment.
problem Limited study on deep learning for system reliability assessment.
method Physics-informed deep learning approach for system reliability assessment.
result Physics-informed deep learning can alleviate computational challenges and combine measurement data and mathematical models.
A new method for high-dimensional RBDO using stochastic emulators.
problem Efficient RBDO in high-dimensional settings.
method Unified stochastic representation, stochastic emulators, deterministic mapping.
result Significant computational gains in high-dimensional settings.
Superpixel-mix enhances reliability in semantic segmentation.
problem Improving reliability in real-world semantic segmentation.
method Superpixel-mix, a new data augmentation method with teacher-student consistency training.
result Superpixel-mix achieves state-of-the-art results in semi-supervised semantic segmentation.
Structural reliability methods aim at computing the probability of failure of systems with respect to some prescribed performance functions. In modern engineering such functions usually resort to running an expensive-to-evaluate computational model (e.g. a finite element model). In this respect simulation methods, whic…
Paper improves ETF tail-risk monitoring reliability.
problem Unreliable ETF risk monitoring under degraded data.
method Combines quality checks, prediction, scoring, and adjustment.
result Improves tail-risk monitoring, especially during stressed periods.
PS-DME evaluates model performance and reliability after data-dependent selection.
problem Evaluating model performance and reliability when data is used for selection and evaluation.
method Post-selection distributional model evaluation (PS-DME) using e-values to control false coverage rate.
result PS-DME provides reliable comparison of model configurations across different reliability levels.
Bayesian framework improves deep classifier reliability.
problem Overconfident models under dataset shift.
method Bayesian inference with out-of-distribution data augmentation.
result Reliable uncertainty estimates for deep classifiers.
MAntRA combines machine learning and Bayesian methods for time-dependent reliability analysis of unknown systems.
problem Time-dependent reliability analysis of systems with unknown governing physics.
method Combines machine learning, Bayesian statistics, and stochastic integration to discover and analyze SDEs from data.
result Demonstrates the effectiveness of MAntRA on three numerical examples, indicating its potential for in-situ and heritage structure analysis.
Paper proposes a method to estimate individual treatment effects reliably from observational data.
problem Estimating individual treatment effects from observational data is challenging and important.
method The approach uses the Information Bottleneck principle to find more reliable representations for ITE estimation.
result The proposed model achieves state-of-the-art results and provides more reliable prediction performances with uncertainty information.
CorrCA identifies reliable dimensions in multivariate data across repetitions.
problem Finding consistent dimensions in multivariate data across trials, subjects, or raters.
method Maximizes the ratio of between-repetition to within-repetition covariance.
result CorrCA leads to repeat-reliability maximization and is equivalent to Linear Discriminant Analysis for zero-mean signals.
Study proposes a statistical testing framework for evaluating clustering pipelines.
problem Quantifying the statistical reliability of clustering results from data analysis pipelines.
method Selective inference-based statistical testing framework for clustering pipelines.
result The proposed test controls the type I error rate and is effective in validating clustering results.
A new method uses physics-informed neural networks to solve reliability analysis problems without simulations.
problem Solving reliability analysis problems without the need for expensive simulations.
method Physics-informed neural networks to learn directly from problem physics.
result Eliminates the need for expensive simulations and achieves highly accurate results.
Study uses machine learning and PolyModel to improve hedge fund performance.
problem Improving hedge fund investment performance with machine learning.
method Integration of machine learning techniques, PolyModel feature selection, and analysis of fund size.
result Machine learning enhances cumulative returns but increases annual volatility.
Paper evaluates synthetic retail data for fidelity, utility, and privacy.
problem Ensuring accurate synthetic data in retail.
method Differentiates between continuous and discrete data, measures fidelity and utility, and uses Differential Privacy for privacy.
result Validated framework for reliable and scalable synthetic data evaluation.
Paper proposes a method to reliably find correlations in categorical data.
problem Discovering reliable correlations in categorical data without distribution assumptions.
method Proposes a corrected-for-chance, consistent, and efficient estimator for normalized total correlation.
result Empirical evaluation shows low-regret optimization outcomes and effective algorithms for both small and large data.
Unstructured data refers to information that does not have a predefined data model or is not organized in a pre-defined manner. Loosely speaking, unstructured data refers to text data that is generated by humans. In after-sales service businesses, there are two main sources of unstructured data: customer complaints, wh…
We consider the problem of option pricing under stochastic volatility models, focusing on the linear approximation of the two processes known as exponential Ornstein-Uhlenbeck and Stein-Stein. Indeed, we show they admit the same limit dynamics in the regime of low fluctuations of the volatility process, under which we …