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Found 6 papers, 3 papers with code
Bayesian Active Learning for Censored Regression
To address this, we derive the entropy and the mutual information for censored distributions and derive the BALD objective for active learning in censored regression ($\mathcal{C}$-BALD).
Deep Evidential Learning for Bayesian Quantile Regression
The proposed method is based on evidential learning, which allows the model to capture aleatoric and epistemic uncertainty with a single deterministic forward-pass model.
Mind the Gap: Modelling Difference Between Censored and Uncensored Electric Vehicle Charging Demand
As a result, machine learning models that rely on these observed records for forecasting charging demand may be limited in their application in future infrastructure expansion and supply management, as they do not estimate the true demand for charging.
Bayesian Active Learning with Fully Bayesian Gaussian Processes
In active learning, where labeled data is scarce or difficult to obtain, neglecting this trade-off can cause inefficient and non-optimal querying, leading to unnecessary data labeling.
Deep Spatio-Temporal Forecasting of Electrical Vehicle Charging Demand
To meet this requirement, accurate forecasting of the charging demand is vital.
Modeling Censored Mobility Demand through Quantile Regression Neural Networks
We address this gap by extending current Censored Quantile Regression models to learn multiple quantiles at once and apply these to synthetic baseline datasets and datasets from two shared mobility providers in the Copenhagen metropolitan area in Denmark.
