Multi Output Regression

Multi-output regression tackles the problem of predicting multiple interrelated outputs simultaneously from a single set of inputs, aiming for improved efficiency and accuracy compared to separate univariate models. Current research emphasizes developing sophisticated model architectures, including deep ensembles, neural networks (especially those incorporating transformers), and gradient boosting methods, often tailored to specific application needs like spatial transcriptomics prediction or multi-step time series forecasting. This field is crucial for advancing various scientific domains and practical applications, as it enables more comprehensive and efficient modeling of complex systems where multiple outputs are inherently linked, improving prediction accuracy and uncertainty quantification. Furthermore, research is actively exploring fairness considerations and efficient methods for handling large datasets and resource-constrained environments.