Editorial: Randomization-Based Deep and Shallow Learning Algorithms

Randomization-based learning offers an efficient alternative to fully iterative training by fixing part of a model’s internal transformation and estimating only a small set of trainable parameters. This paradigm can substantially reduce training time and computational demand while retaining competitive predictive performance. It also provides a distinctive scientific lens through which the architectural bias and representational capacity of neural systems can be investigated in the absence of iterative learning. This editorial introduces the Special Issue on Randomization-Based Deep and Shallow Learning Algorithms, which brings together twelve contributions covering theoretical and architectural developments, systematic benchmarking, hybrid deep-randomized models, uncertainty quantification, and applications in computer vision, neuroimaging, renewable energy forecasting, industrial process modeling, intelligent transportation, maritime safety, and network science. Collectively, the papers demonstrate that randomization-based learning is evolving from a family of shallow, computationally efficient learners into a broader design principle for deep, ensemble, and uncertainty-aware systems. The Special Issue also exposes open challenges concerning principled random-feature design, reproducible benchmarking, scalable linear solvers, robustness, calibration, interpretability, and deployment under streaming and resource-constrained conditions.