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.
In many instances, information on engineering systems can be obtained through measurements, monitoring or direct observations of system performances and can be used to update the system reliability estimate. In structural reliability analysis, such information is expressed either by inequalities (e.g. for the observati…
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.
Proposes a Koopman operator method for time-dependent reliability analysis of nonlinear systems.
problem Challenges in time-dependent reliability analysis of nonlinear dynamical systems.
method Koopman operator approach for transforming nonlinear systems into linear ones, combined with deep learning for intrinsic coordinates.
result Robust and generalizable approach for time-dependent reliability analysis, superior to purely data-driven methods.
BeMF improves recommendation reliability in recommender systems.
problem Improving reliability in recommender systems beyond accuracy.
method Bernoulli Matrix Factorization (BeMF) for model-based collaborative filtering.
result BeMF selects more reliable predictions, improving recommendation quality.
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.
Economics tool predicts failure times in reliability systems.
problem Predicting optimal failure times in weighted k-out-of-n reliability systems with heterogeneous component failure.
method Using rational expectations to analyze and predict failure times in reliability systems with heterogeneous component failure.
result Different measures are optimal for predicting system failure depending on component failure distributions.
New metric assesses reliability of AI explanations.
problem Unreliable AI explanations under realistic conditions.
method Explanation Reliability Index (ERI) metrics quantifying stability under four axioms.
result Widespread reliability failures in popular explanation methods.
CoNBONet improves reliability analysis of complex systems with fast, energy-efficient predictions.
problem Time-dependent reliability analysis of nonlinear systems under stochastic excitations is computationally demanding.
method CoNBONet combines deep operator networks with neuroscience-inspired neuron models for fast, energy-efficient inference.
result CoNBONet provides reliable coverage of failure probabilities with theoretical guarantees.
The paper certifies AI reliability via sampling and calibration, providing exact guarantees.
problem Ensuring trust in black-box AI systems' outputs.
method Self-consistency sampling and conformal calibration.
result Reliability levels derived from these methods offer finite-sample guarantees.
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.
New AI model improves grid planning efficiency and reliability.
problem Improving distribution grid planning with AI for energy sustainability.
method Hyperstructures Graph Convolutional Neural Networks (Hyper-GCNNs) with attention mechanism.
result Hyper-GCNNs outperforms existing models in computational efficiency and accuracy.
Reliability Options are capacity remuneration mechanisms aimed at enhancing security of supply in electricity systems. They can be framed as call options on electricity sold by power producers to System Operators. This paper provides a comprehensive mathematical treatment of Reliability Options. Their value is first de…
Natural disasters can have catastrophic impacts on the functionality of infrastructure systems and cause severe physical and socio-economic losses. Given budget constraints, it is crucial to optimize decisions regarding mitigation, preparedness, response, and recovery practices for these systems. This requires accurate…
The paper introduces recklessness to improve recommendation quality and quantity.
problem The reliability/coverage dilemma in recommender systems limits the number of recommended items.
method Incorporates a new term (recklessness) into matrix factorization-based recommender systems to address the dilemma.
result Recklessness improves the quantity and quality of recommendations by allowing for risk regulation.
AL-SPCE improves reliability analysis for complex systems with active learning and SPCE.
problem Efficiently analyzing reliability of complex, computationally expensive models with intrinsic randomness.
method Active learning framework using stochastic polynomial chaos expansions (SPCE) to reduce computational burden.
result AL-SPCE maintains high accuracy in reliability estimates while significantly improving efficiency.
This dissertation tackles challenges in reliable machine learning measurement.
problem Challenges in reproducibility, scalability, and uncertainty quantification in machine learning.
method Develops criteria for meaningful metrics and methodologies for scalable, reliable measurement.
result Provides methods for evaluating generative-AI systems and quantifying memorization.
New method improves reliability of recommender systems in face of fake accounts and manipulation.
problem Improving reliability of recommender systems in the presence of fake accounts and manipulation.
method Robust Discrete Matrix Completion (RDMC) method designed to handle sparse rating data and manipulation.
result Evaluations show RDMC offers a statistically-sound blueprint for future studies on recommender systems.
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.
New framework tackles high-dimensional reliability analysis using surrogate models and active subspaces.
problem High computational cost and curse of dimensionality in reliability analysis of high-dimensional systems.
method Sparse Active Subspace (SAS) algorithm for identifying low-dimensional manifolds and constructing efficient surrogate models.
result Proposed framework significantly improves accuracy and efficiency of reliability analysis compared to existing methods.
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.
The paper improves recommendation systems by ensuring their outputs are reliable.
problem Recommendation systems often lack reliability guarantees for their outputs.
method The method uses a pre-trained ranking model to create a set of items with rigorous FDR control.
result The approach provides a way to guarantee the reliability of recommendation outputs.
Develop a framework to evaluate the reliability of probabilistic emulation of physical systems.
problem Evaluating the reliability of probabilistic forecasts in physical systems.
method Developing a framework to assess the reliability of probabilistic emulation across diverse 2D spatiotemporal systems.
result CRPS-trained ensembles achieve more reliable uncertainties on single-step prediction and autoregressive rollouts.
A new method estimates rare failure events in complex systems.
problem Estimating the probability of rare failure events in non-linear systems.
method Stochastic Spectral Embedding (SSE) combined with modifications for efficient rare event estimation.
result Rare failure probability decomposed into conditional probabilities for easier computation.
Paper proposes NeuroAttack to undermine SNNs security through bit-flips.
problem Security and reliability issues in SNNs.
method Cross-layer attack exploiting low-level reliability issues via adversarial input noise.
result Serious integrity threat to SNNs and DNNs.
Benchmark evaluates AI-generated financial QA hallucinations, highlighting system vulnerabilities.
problem Ensuring factual accuracy of AI-generated financial QA outputs.
method Developed a benchmark dataset and evaluated six detection methods under clean and noisy conditions.
result LLM-based judges and embedding methods perform best, but degrade under noisy conditions.
MLDemon monitors ML systems post-deployment, improving reliability with real-time performance estimates and expert labels.
problem Ensuring reliability of machine learning systems post-deployment, especially when user inputs differ from training data.
method Integrates unlabeled and on-demand labeled data to monitor ML model performance in real-time, deciding when to acquire expert labels.
result Outperforms existing approaches in temporal datasets with diverse distribution drifts, providing theoretical optimality for distribution drifts.
We combine Bayesian networks (BNs) and structural reliability methods (SRMs) to create a new computational framework, termed enhanced Bayesian network (eBN), for reliability and risk analysis of engineering structures and infrastructure. BNs are efficient in representing and evaluating complex probabilistic dependence …
Study improves reliability of neural models for virtual screening.
problem Reliability issues in neural models for molecular property prediction.
method Investigated model architectures, regularization, and loss functions.
result Correct choice of regularization and inference methods improves reliability.
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.
This paper improves robot grasping by integrating meta-control and latent-space imagination.
problem Dual-system approaches fail to consider the reliability of the learned model when making multiple-step predictions.
method A meta-controller arbitrates between model-based and model-free decisions based on local reliability, encouraging actions that improve the model and generating imagined experiences for additional training.
result Our approach learns near-optimal grasping policies in dense- and sparse-reward environments, outperforming baseline and state-of-the-art methods.
New method combines neural networks with Monte Carlo for complex system reliability.
problem Estimating small failure probabilities in complex systems.
method Subset Simulation with Hamiltonian Neural Networks.
result High acceptance rates and computational efficiency in low-probability regions.
Probabilistic SR method speeds up high-fidelity simulations with reliable uncertainty estimates.
problem Lack of reliable uncertainty quantification in deep-learning based SR methods.
method Statistical Finite Element Method and energy-based generative modeling.
result Efficient high-resolution predictions with inherent uncertainty estimates.
Kernel methods accurately predict Hamiltonian systems from data.
problem Data-driven simulation of Hamiltonian systems.
method Two-step and one-step kernel-based methods for identifying and forecasting Hamiltonian systems.
result Framework achieves accurate, data-efficient predictions across various benchmark systems.
Open-source Vizier optimizes complex systems for Google and beyond.
problem Optimizing large-scale systems with multiple objectives and constraints.
method Distributed, fault-tolerant, flexible API for blackbox optimization.
result OSS Vizier supports a wide range of optimization problems and is available as open-source.
Crowdsourcing systems, in which numerous tasks are electronically distributed to numerous "information piece-workers", have emerged as an effective paradigm for human-powered solving of large scale problems in domains such as image classification, data entry, optical character recognition, recommendation, and proofread…
In this paper, a novel experienced deep reinforcement learning (deep-RL) framework is proposed to provide model-free resource allocation for ultra reliable low latency communication (URLLC). The proposed, experienced deep-RL framework can guarantee high end-to-end reliability and low end-to-end latency, under explicit …
Intelligent transportation systems (ITSs) will be a major component of tomorrow's smart cities. However, realizing the true potential of ITSs requires ultra-low latency and reliable data analytics solutions that can combine, in real-time, a heterogeneous mix of data stemming from the ITS network and its environment. Su…
AutoRec automates deep recommendation models using AutoML.
problem Adapting to changing data and tasks in recommender systems.
method AutoML platform for model search and hyperparameter tuning.
result Identifies models similar to the best model without prior knowledge.
Reliability is a critical consideration to DL-based systems. But the statistical nature of DL makes it quite vulnerable to invalid inputs, i.e., those cases that are not considered in the training phase of a DL model. This paper proposes to perform data sanity check to identify invalid inputs, so as to enhance the reli…
New metrics improve understanding of predictive system reliability.
problem Evaluating conditional coverage of predictive systems.
method Casting conditional coverage estimation as a classification problem, using excess risk of the target coverage (ERT) metrics.
result Modern classifiers provide higher statistical power for estimating conditional coverage.
We investigate the problem of reliable communication between two legitimate parties over deletion channels under an active eavesdropping (aka jamming) adversarial model. To this goal, we develop a theoretical framework based on probabilistic finite-state automata to define novel encoding and decoding schemes that ensur…
Algorithm optimizes and infers performance online, improving reliability.
problem Balancing optimization and statistical inference in complex systems.
method Online algorithm that provides consistent performance variance and confidence intervals.
result Online consistent estimator for performance variance and asymptotic confidence intervals.
We consider a multivariate default system where random environmental information is available. We study the dynamics of the system in a general setting and adopt the point of view of change of probability measures. We also make a link with the density approach in the credit risk modelling. In the particular case where …
Study finds machine learning interpretations are often unstable and unreliable.
problem Reliability of machine learning interpretations in high-stakes domains.
method Stability study on global interpretations using tabular data.
result Popular interpretation methods are frequently unstable, less stable than predictions, and not associated with prediction accuracy.
Statistical guarantees for hyperparameter selection
problem Hyperparameter selection in AI systems
method Learn-then-test framework
result Provable reliability and safety
AI systems need reliable testing to ensure safety and trustworthiness.
problem Current AI Act lacks functional trustworthiness for AI systems.
method Define technical application distribution, set risk-based performance, and conduct statistically valid testing.
result Reliable functional trustworthiness is essential for AI systems.
Paper extends SI method for detecting CPs in complex systems' frequency domain.
problem Identifying change points in complex systems' frequency domain.
method Extends SI framework to frequency domain using DFT properties and develops valid p-values.
result Reliable detection of genuine CPs with strong statistical guarantees.