Evaluation of a sampling approach for computationally efficient uncertainty quantification in regression learning models

The capability of effectively quantifying the uncertainty associated to a given prediction is an important task in many applications that range from drug design to autonomous driving, providing valuable information to many downstream decision making processes. The increasing capacity of novel machine learning models, and the growing amount of data on which these systems are trained poses however significant issues to be addressed. Recent research advocated the need for evaluating learning systems not only according to traditional accuracy metrics but also according to the computational complexity required to design them, towards a perspective of sustainability and inclusivity. In this work, we present an empirical investigation aimed at assessing the impact of uniform sampling on the reduction of computational requirements, the quality of regression, and on its uncertainty quantification. We performed several experiments with recent state-of-the art methods characterized by statistical guarantees whose performances have been measured according to different metrics for evaluating uncertainty quantification (i.e. coverage and length of prediction intervals) and regression (i.e. errors measures and correlation). Experimental results highlight possible interesting trade-offs between computation time, regression and uncertainty evaluation quality, thus confirming the viability of sampling-based approaches to overcome computational bottlenecks without significantly affecting the quality of predictions.