Efficient Decomposed Learning for Structured Prediction

TL;DR

This paper introduces Decomposed Learning (DecL), an efficient approach for structured prediction that restricts inference to manageable parts of the structured space, maintaining accuracy while reducing computational complexity.

Contribution

The paper proposes DecL, a novel method that enables efficient structured prediction learning by leveraging structure-based restrictions, with theoretical guarantees and practical effectiveness.

Findings

DecL is as accurate as exact learning in real-world scenarios.

DecL significantly reduces inference complexity.

Theoretical conditions for DecL's equivalence to exact learning are established.

Abstract

Structured prediction is the cornerstone of several machine learning applications. Unfortunately, in structured prediction settings with expressive inter-variable interactions, exact inference-based learning algorithms, e.g. Structural SVM, are often intractable. We present a new way, Decomposed Learning (DecL), which performs efficient learning by restricting the inference step to a limited part of the structured spaces. We provide characterizations based on the structure, target parameters, and gold labels, under which DecL is equivalent to exact learning. We then show that in real world settings, where our theoretical assumptions may not completely hold, DecL-based algorithms are significantly more efficient and as accurate as exact learning.