MACRO: A Meta-Algorithm for Conditional Risk Minimization
This addresses a fundamental problem in machine learning for sequential prediction tasks, though it appears incremental as it builds on theoretical algorithms with practical improvements.
The paper tackles the problem of conditional risk minimization (CRM) for sequentially arriving dependent data, introducing MACRO, a meta-algorithm that avoids storing all data and offers learning guarantees, yielding improved prediction performance compared to traditional non-conditional learning.
We study conditional risk minimization (CRM), i.e. the problem of learning a hypothesis of minimal risk for prediction at the next step of sequentially arriving dependent data. Despite it being a fundamental problem, successful learning in the CRM sense has so far only been demonstrated using theoretical algorithms that cannot be used for real problems as they would require storing all incoming data. In this work, we introduce MACRO, a meta-algorithm for CRM that does not suffer from this shortcoming, but nevertheless offers learning guarantees. Instead of storing all data it maintains and iteratively updates a set of learning subroutines. With suitable approximations, MACRO applied to real data, yielding improved prediction performance compared to traditional non-conditional learning.