Learning Reductions that Really Work

Abstract

We provide a summary of the mathematical and computational techniques that have enabled learning reductions to effectively address a wide class of problems, and show that this approach to solving machine learning problems can be broadly useful.

References in corpus (2)

• Search-based Structured Prediction
• Online Importance Weight Aware Updates

Cited by in corpus (1)

• Loss factorization, weakly supervised learning and label noise robustness