Papers Prediction Intervals
“Prediction Intervals” 태그가 달린 논문 309편 · 필터 해제
Foundation models for time series forecasting: Application in conformal prediction
The zero-shot capabilities of foundation models (FMs) for time series forecasting offer promising potentials in conformal prediction, as most of the available data can be allocated to calibration. This study compares the…
Conformal PredictionPredictionPrediction IntervalsTime Series+1A Wireless Foundation Model for Multi-Task Prediction
With the growing complexity and dynamics of the mobile communication networks, accurately predicting key system parameters, such as channel state information (CSI), user location, and network traffic, has become essentia…
modelPredictionPrediction IntervalsOn the relationship between prediction intervals, tests of sharp nulls and inference on realized treatment effects in settings with few treated units
We study how inference methods for settings with few treated units that rely on treatment effect homogeneity extend to alternative inferential targets when treatment effects are heterogeneous -- namely, tests of sharp nu…
Prediction IntervalsvalidLLM-Powered CPI Prediction Inference with Online Text Time Series
Forecasting the Consumer Price Index (CPI) is an important yet challenging task in economics, where most existing approaches rely on low-frequency, survey-based data. With the recent advances of large language models (LL…
Prediction IntervalsTime SeriesDiffusion-based Time Series Forecasting for Sewerage Systems
We introduce a novel deep learning approach that harnesses the power of generative artificial intelligence to enhance the accuracy of contextual forecasting in sewerage systems. By developing a diffusion-based model that…
Prediction IntervalsTime SeriesTime Series ForecastingDeep Learning-Based BMD Estimation from Radiographs with Conformal Uncertainty Quantification
Limited DXA access hinders osteoporosis screening. This proof-of-concept study proposes using widely available knee X-rays for opportunistic Bone Mineral Density (BMD) estimation via deep learning, emphasizing robust unc…
Conformal PredictionPrediction IntervalsUncertainty QuantificationIndividualised Counterfactual Examples Using Conformal Prediction Intervals
Counterfactual explanations for black-box models aim to pr ovide insight into an algorithmic decision to its recipient. For a binary classification problem an individual counterfactual details which features might be cha…
Binary ClassificationConformal PredictioncounterfactualData Augmentation+2STACI: Spatio-Temporal Aleatoric Conformal Inference
Fitting Gaussian Processes (GPs) provides interpretable aleatoric uncertainty quantification for estimation of spatio-temporal fields. Spatio-temporal deep learning models, while scalable, typically assume a simplistic i…
Gaussian ProcessesGPUPrediction IntervalsUncertainty Quantification+1MetaSTNet: Multimodal Meta-learning for Cellular Traffic Conformal Prediction
Network traffic prediction techniques have attracted much attention since they are valuable for network congestion control and user experience improvement. While existing prediction techniques can achieve favorable perfo…
Conformal PredictionMeta-LearningNetwork Congestion ControlPrediction+2Unveil Sources of Uncertainty: Feature Contribution to Conformal Prediction Intervals
Cooperative game theory methods, notably Shapley values, have significantly enhanced machine learning (ML) interpretability. However, existing explainable AI (XAI) frameworks mainly attribute average model predictions, o…
AttributeConformal PredictionFeature ImportancePrediction IntervalsExtreme Conformal Prediction: Reliable Intervals for High-Impact Events
Conformal prediction is a popular method to construct prediction intervals for black-box machine learning models with marginal coverage guarantees. In applications with potentially high-impact events, such as flooding or…
Conformal PredictionPredictionPrediction Intervalsquantile regressionConformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach
Conformal prediction is a powerful tool for constructing prediction intervals for black-box models, providing a finite sample coverage guarantee for exchangeable data. However, this exchangeability is compromised when so…
Conformal PredictionImputationOutlier DetectionPrediction+1A Minimax-MDP Framework with Future-imposed Conditions for Learning-augmented Problems
We study a class of sequential decision-making problems with augmented predictions, potentially provided by a machine learning algorithm. In this setting, the decision-maker receives prediction intervals for unknown para…
Decision MakingPrediction IntervalsSequential Decision MakingModel uncertainty quantification using feature confidence sets for outcome excursions
When implementing prediction models for high-stakes real-world applications such as medicine, finance, and autonomous systems, quantifying prediction uncertainty is critical for effective risk management. Traditional app…
PredictionPrediction IntervalsUncertainty QuantificationFrom predictions to confidence intervals: an empirical study of conformal prediction methods for in-context learning
Transformers have become a standard architecture in machine learning, demonstrating strong in-context learning (ICL) abilities that allow them to learn from the prompt at inference time. However, uncertainty quantificati…
Conformal PredictionIn-Context LearningPredictionPrediction Intervals+2Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning
This paper explores uncertainty quantification (UQ) methods in the context of Kolmogorov-Arnold Networks (KANs). We apply an ensemble approach to KANs to obtain a heuristic measure of UQ, enhancing interpretability and r…
Conformal PredictionKolmogorov-Arnold NetworksPredictionPrediction Intervals+1Adapting GT2-FLS for Uncertainty Quantification: A Blueprint Calibration Strategy
Uncertainty Quantification (UQ) is crucial for deploying reliable Deep Learning (DL) models in high-stakes applications. Recently, General Type-2 Fuzzy Logic Systems (GT2-FLSs) have been proven to be effective for UQ, of…
Computational EfficiencyPrediction IntervalsUncertainty QuantificationConfEviSurrogate: A Conformalized Evidential Surrogate Model for Uncertainty Quantification
Surrogate models, crucial for approximating complex simulation data across sciences, inherently carry uncertainties that range from simulation noise to model prediction errors. Without rigorous uncertainty quantification…
Conformal PredictionPredictionPrediction IntervalsUncertainty QuantificationOnline Selective Conformal Prediction: Errors and Solutions
In online selective conformal inference, data arrives sequentially, and prediction intervals are constructed only when an online selection rule is met. Since online selections may break the exchangeability between the se…
Conformal PredictionPredictionPrediction IntervalsvalidNeuroSep-CP-LCB: A Deep Learning-based Contextual Multi-armed Bandit Algorithm with Uncertainty Quantification for Early Sepsis Prediction
In critical care settings, timely and accurate predictions can significantly impact patient outcomes, especially for conditions like sepsis, where early intervention is crucial. We aim to model patient-specific reward fu…
Conformal PredictionDecision MakingDecision Making Under UncertaintyMulti-Armed Bandits+3