Neural Active Learning Meets the Partial Monitoring Framework

Maxime Heuillet, Ola Ahmad, Audrey Durand

Keywords: online learning, active learning, partial monitoring, online classification

TL;DR: We propose the first neural partial monitoring strategy, and demonstrate its potential on online classification tasks.

Abstract:

We focus on the online-based active learning (OAL) setting where an agent operates over a stream of observations and trades-off between the costly acquisition of information (labelled observations) and the cost of prediction errors.
We propose a novel foundation for OAL tasks based on partial monitoring, a theoretical framework specialized in online learning from partially informative actions.
We show that previously studied binary and multi-class OAL tasks are instances of partial monitoring.
We expand the real-world potential of OAL by introducing a new class of cost-sensitive OAL tasks.
We propose NeuralCBP, the first PM strategy that accounts for predictive uncertainty with deep neural networks.
Our extensive empirical evaluation on open source datasets shows that NeuralCBP has competitive performance against state-of-the-art baselines on multiple binary, multi-class and cost-sensitive OAL tasks.

List Of Authors: Heuillet, Maxime and Ahmad, Ola and Durand, Audrey

Code Url: https://github.com/MaxHeuillet/neuralCBPside

Submission Number: 445