RUE audits machine learning predictions for reliability.
problem Ensuring trust in machine learning models for high-stakes applications.
method Resampling uncertainty estimation (RUE) algorithm to audit model reliability after training.
result RUE more effectively detects inaccurate predictions than existing tools.
Bayesian learning improves reliability of molecular predictions for hit compound discovery.
problem Improving reliability of machine learning predictions for virtual screening.
method Bayesian learning algorithms applied to graph neural networks.
result Bayesian learning leads to well-calibrated predictions and higher hit compound success.
Paper predicts interference for better LA in URLLC.
problem Improving LA for URLLC with strict latency and reliability.
method Exploits time correlation of interference for prediction.
result Predicted interference improves LA for URLLC.
Paper introduces conformal prediction for reliable uncertainty quantification in landmark localization.
problem Systematic underestimation of total predictive uncertainty in landmark localization.
method Conformal prediction framework for multi-output regression, generating flexible prediction regions.
result Methods outperform existing approaches in validity and efficiency across 2D and 3D datasets.
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.
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 introduces methods for more reliable probabilistic predictions with confidence intervals.
problem Inaccurate labeling of datasets due to unreliable probabilistic predictions from weak labeling functions.
method Proposes a methodology to provide confidence intervals for label probabilities using uncertainty sets of distributions.
result Improves reliability of probabilistic predictions and provides confidence intervals for label probabilities.
New method improves reliability of selecting individuals based on predicted treatment effects.
problem Reliability of selecting individuals based on predicted conditional average treatment effects (CATE) is unreliable.
method Denoised Conformal Alignment, combining proxy errors, variance estimation, and Benjamini-Hochberg selection.
result Significantly improved power in selecting individuals while maintaining false discovery rate control.
This study compares two methods for uncertainty estimation in CNNs, finding Conformal Prediction more reliable.
problem CNNs often overestimate uncertainty, leading to unreliable predictions.
method Bayesian approximation via Monte Carlo Dropout and Conformal Prediction.
result Conformal Prediction produces more reliable uncertainty estimates than Monte Carlo Dropout.
New approach links machine learning reliability to epistemic uncertainty.
problem Characterize and quantify reliability of machine learning predictions.
method Extend JTB theory to neural networks, linking prediction reliability to support characteristics.
result Demonstrates reliability for individual predictions and identifies regions of uncertainty.
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.
Proposes a new criterion for reliable uncertainty estimation in deep neural networks.
problem Inability of existing approaches to provide reliable uncertainty estimates for deep neural networks.
method Develops a density uncertainty layer architecture that satisfies the proposed criterion.
result Density uncertainty layers provide more reliable uncertainty estimates and robust out-of-distribution detection.
Foundation models improve time series prediction reliability, especially with limited data.
problem Improving time series prediction reliability with limited data.
method Comparison of Time Series Foundation Models (TSFMs) with traditional methods in conformal prediction.
result TSFMs provide more reliable conformalized prediction intervals and more stable calibration with limited data.
Improved reliability of machine learning predictions using variational auto-encoders.
problem Individual unreliability of machine learning models.
method Modified variational auto-encoders to identify a low-dimensional space for reliable classification.
result Improved reliability of predictions and robust identification of adversarial samples.
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.
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.
CPA framework assesses conditional validity of conformal prediction.
problem Challenges in evaluating conditional validity of conformal prediction.
method Reframes conditional coverage evaluation as a supervised learning task.
result Establishes convergence rates and proves CVI consistency.
Framework for reliable prediction errors using Test-Time Dropout and Conformal Prediction.
problem Lack of reliable error computation for deep neural networks in drug discovery.
method Training a single neural network with dropout, applying it multiple times to validation and test sets, generating ensemble predictions, and using Conformal Prediction to compute errors.
result Dropout Conformal Predictors are valid and efficient, with narrower confidence intervals than RF-based Conformal Predictors.
DW-KNN improves KNN by integrating distance and neighbor reliability for better prediction accuracy.
problem Standard KNN assumes all neighbors are equally reliable, leading to unreliable predictions in heterogeneous feature spaces.
method DW-KNN integrates exponential distance with neighbor validity, providing instance-level interpretability and reducing hyperparameter sensitivity.
result DW-KNN achieves 0.8988 average accuracy, ranks 2nd among six methods, and has the lowest cross-validation variance.
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.
The paper improves NN predictions with reliable confidence intervals.
problem Creating accurate prediction intervals for neural network outputs.
method Introduces Conformal Prediction (CP) for NNs, ensuring reliable confidence measures.
result The proposed method produces well-calibrated and tight prediction intervals.
COPP provides reliable intervals for outcomes under a new policy in contextual bandits.
problem Lack of reliable predictive intervals for outcomes under a new policy in contextual bandits.
method Conformal prediction applied to contextual bandits.
result COPP provides finite-sample guarantees without additional assumptions.
New framework improves reliability of learned representations by modeling uncertainty and structural constraints.
problem Uncertainty in learned representations treated as deterministic, leading to unreliable models.
method Proposes a principled framework for reliable representation learning with uncertainty-aware regularization and structural constraints.
result Improves stability, calibration, and robustness of learned representations.
The paper proposes a method to assess when automated predictions are reliable.
problem Ensuring reliability and safety of automated decision-making in machine learning.
method Clustering to measure distances between outputs and class centroids, defining a safety threshold based on these distances.
result The proposed metric can efficiently determine when automated predictions are acceptable and when they should be deferred.
Adaptive Quantum Conformal Prediction improves reliability of quantum machine learning predictions.
problem Quantum machine learning lacks robust uncertainty quantification methods.
method Adaptive Conformal Inference applied to quantum conformal prediction to maintain validity over time.
result AQCP achieves target coverage levels and is more stable than standard quantum conformal prediction.
Enhances deep learning models' robustness against adversarial attacks.
problem Lack of reliable uncertainty estimates and robust defenses for deep learning models.
method Integrates Conformal Prediction principles with adversarial training.
result Introduces OPSA-AT, a defense strategy that enhances robustness and reliability.
New model predicts hate speech with uncertainty estimates.
problem Detect and explain hate speech without restricting free speech.
method Adapted deep neural networks with Monte Carlo dropout.
result Models can reliably assess when texts are borderline.
New method provides reliable high-confidence prediction intervals for high-impact events.
problem High-impact events require very high confidence prediction intervals, but classical methods provide uninformative intervals.
method Bridge extreme value statistics and conformal prediction to provide reliable and informative prediction intervals.
result Provides reliable and informative prediction intervals with high-confidence coverage.
The paper proposes a method to calibrate healthcare AI models for reliability and interpretability.
problem Characterizing model reliability and enabling introspection of model behavior in clinical decision making.
method A calibration-driven learning method combined with interpretability techniques based on counterfactual reasoning.
result Demonstrates the effectiveness of the proposed approach using a lesion classification problem with dermoscopy images.
The ACCRU framework improves probabilistic forecasts by capturing input-dependent uncertainty.
problem Uncertainty in deterministic predictions, especially for skewed and non-Gaussian errors.
method Neural network trained with a loss function balancing accuracy and reliability to learn input-dependent, non-Gaussian uncertainty distributions.
result Improves probabilistic forecasts relative to existing methods, capturing skewed and non-Gaussian errors.
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.
DS-CP improves reliability of uncertainty quantification for large language models under domain shift.
problem Overconfident and factually incorrect outputs (hallucinations) from large language models.
method Adapts conformal prediction to large language models under domain shift by reweighting calibration samples.
result DS-CP delivers more reliable coverage than standard conformal prediction, especially under substantial distribution shifts.
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.
New ML framework for reliable and explainable material predictions.
problem Challenges in applying ML to materials science, especially with imbalanced data.
method Proposes a general-purpose explainable and reliable machine-learning framework using ensembles of simpler models.
result Demonstrates improved reliability and explainability in material property predictions.
New decision-theoretic calibration error metric improves prediction reliability.
problem Improving the reliability of predictions for decision-making.
method Proposed Calibration Decision Loss (CDL) and an efficient algorithm to achieve near-optimal CDL.
result Near-optimal CDL guarantees vanishing payoff loss from miscalibration.
This thesis enhances ML reliability by selectively abstaining from predictions when uncertain.
problem Improving reliability in machine learning systems, especially in high-stakes domains.
method Exploiting uncertainty signals from training trajectories to develop lightweight, post-hoc abstention methods compatible with differential privacy.
result A robust trajectory-based approach to selective prediction that maintains high accuracy under privacy noise.
New methods for distributed CP improve reliability in healthcare.
problem Post-hoc calibration of models in safety-critical domains.
method Quantile-based and histogram-based distributed conformal prediction via message passing.
result Improved reliability and coverage guarantees in decentralized settings.
Plots show miscalibration directly as slopes of secant lines.
problem Detecting discrepancies between probabilistic predictions and actual outcomes.
method Cumulative differences between observed and expected values displayed as slopes of secant lines.
result Directly shows miscalibration without binning or kernel density estimation.
CP provides reliable prediction intervals for short-term power markets.
problem Short-term electricity price forecasting in power markets.
method Conformal Prediction (CP) integrated with various point forecast models.
result CP yields sharp and reliable prediction intervals in short-term power markets.
Neural program analyzers are brittle and sensitive to input changes.
problem Reliability of neural program analyzers in software engineering.
method Developing testing techniques for neural program analyzers.
result Simple input perturbations can cause neural program analyzers to make mistakes.
Spatial variable selection is crucial for reliable spatial predictions in machine learning.
problem Spatial autocorrelation leads to overfitting and poor spatial predictions.
method Used Random Forests with non-spatial and spatial cross-validation strategies.
result Spatial variable selection is essential for reliable spatial predictions.
New robustness metric helps select reliable classifiers.
problem Evaluating reliability of classifier predictions.
method Proposed new robustness metric for any classifier and feature type.
result Demonstrated ability to distinguish reliable from unreliable predictions.
New method uses interval-based metric to validate prediction uncertainty in machine learning.
problem Validation of prediction uncertainty in machine learning regression tasks is unreliable due to heavy-tailed distributions.
method Shift from variance-based metrics to interval-based Prediction Interval Coverage Probability (PICP).
result PICP method more quickly and reliably tests prediction intervals than variance-based metrics.
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.
Diverging Flows detects extrapolations in flow models, ensuring reliable predictions.
problem Flow models extrapolate into invalid data, leading to silent failures.
method Structurally enforce inefficient transport for off-manifold inputs.
result Effective detection of extrapolations without compromising predictive fidelity or inference latency.
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.
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.
A new method calibrates value predictions in offline RL to improve reliability.
problem Difficulty in long-horizon value prediction in offline reinforcement learning.
method Bellman calibration, a weak reliability criterion, and Iterated Bellman Calibration.
result Finite-sample guarantees show that Bellman calibration error is controlled at nonparametric rates.