Ensemble Based

Ensemble-based methods are a powerful approach in machine learning and scientific computing, aiming to improve prediction accuracy, robustness, and uncertainty quantification by combining multiple models. Current research focuses on applying ensembles to diverse problems, including reinforcement learning (using techniques like Q-ensembles and diverse priors), scientific surrogate modeling (leveraging normalizing flows), and various classification tasks (employing deep neural networks, random forests, and BERT-based models). The resulting improvements in performance, reliability, and uncertainty estimation have significant implications across numerous scientific fields and practical applications, from subsurface characterization to anomaly detection in satellite data.