REINFORCEMENT LEARNING

CORE MACHINE LEARNING

TaskMet: Task-driven Metric Learning for Model Learning

December 11, 2023

Abstract

Deep learning models are often used with some downstream task. Models solely trained to achieve accurate predictions may struggle to perform well on the desired downstream tasks. We propose using the task loss to learn a metric which parameterizes a loss to train the model. This approach does not alter the optimal prediction model itself, but rather changes the model learning to emphasize the information important for the downstream task. This enables us to achieve the best of both worlds: a prediction model trained in the original prediction space while also being valuable for the desired downstream task. We validate our approach through experiments conducted in two main settings: 1) decision-focused model learning scenarios involving portfolio optimization and budget allocation, and 2) reinforcement learning in noisy environments with distracting states.

Download the Paper

AUTHORS

Written by

Dishank Bansal

Ricky Chen

Mustafa Mukadam

Brandon Amos

Publisher

NeurIPS

Research Topics

Reinforcement Learning

Core Machine Learning

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