Structured Prediction

Structured prediction studies how to make interdependent decisions, such as labeling a sentence, assembling an object set, extracting a diagram’s topology, or assigning coherent labels in a medical hierarchy. Common approaches include structured losses and decoding, set prediction with matching, autoregressive selection, and models that encode constraints or topology. The field spans language, vision, scientific measurement, and time-series tasks, with evaluations often testing both task accuracy and properties such as consistency, connectivity, and robustness to ambiguity. A central tension is how to capture dependencies among outputs while keeping inference efficient and ensuring predictions remain reliable under distribution shifts and difficult subgroups.