HC-Search: Learning Heuristics and Cost Functions for Structured Prediction

• Published in: Proceedings of the ... AAAI Conference on Artificial Intelligence 2013-06, Vol.27 (1), p.253-259
• Main Authors: Doppa, Janardhan Rao, Fern, Alan, Tadepalli, Prasad
• Format: Article
• Language: English
• ISSN: 2159-5399; 2374-3468
• DOI: 10.1609/aaai.v27i1.8664

Structured prediction is the problem of learning a function from structured inputs to structured outputs with prototypical examples being part-of-speech tagging and image labeling. Inspired by the recent successes of search-based structured prediction, we introduce a new framework for structured prediction called {\em HC-Search}. Given a structured input, the framework uses a search procedure guided by a learned heuristic H to uncover high quality candidate outputs and then uses a separate learned cost function C to select a final prediction among those outputs. We can decompose the regret of the overall approach into the loss due to H not leading to high quality outputs, and the loss due to C not selecting the best among the generated outputs. Guided by this decomposition, we minimize the overall regret in a greedy stage-wise manner by first training H to quickly uncover high quality outputs via imitation learning, and then training C to correctly rank the outputs generated via H according to their true losses. Experiments on several benchmark domains show that our approach significantly outperforms the state-of-the-art methods.