We propose a penalized nonparametric approach to estimating the quantile regression
process (QRP) in a nonseparable model using rectifier quadratic unit (ReQU) activated deep …

This paper introduces Conformal Thresholded Intervals (CTI), a novel conformal regression
method that aims to produce the smallest possible prediction set with guaranteed coverage …

We propose a penalized nonparametric approach to estimating the quantile regression
process (QRP) in a nonseparable model using rectifier quadratic unit (ReQU) activated deep …

The development of multicore-based embedded real-time systems is a complex process that
encompasses several phases. During the software design and development phases (DDP) …

This paper considers doing quantile regression on censored data using neural networks
(NNs). This adds to the survival analysis toolkit by allowing direct prediction of the target …

Despite the major progress of deep models as learning machines, uncertainty estimation
remains a major challenge. Existing solutions rely on modified loss functions or architectural …

We propose a nonparametric quantile regression method using deep neural networks with a
rectified linear unit penalty function to avoid quantile crossing. This penalty function is …

Machine learning often lacks transparent performance indicators, especially in generating
point predictions. This paper addresses this limitation through conformal prediction, a non …

In application domains requiring mission-critical decision making, such as finance and
robotics, the optimal policy derived by reinforcement learning (RL) often hinges on a …

Quantile regression (QR) is a powerful tool for estimating one or more conditional quantiles
of a target variable $\mathrm {Y} $ given explanatory features $\boldsymbol {\mathrm {X}} …