# Neural Active Learning Meets the Partial Monitoring Framework

arXiv: https://arxiv.org/abs/2405.08921 · HTML: https://mlsanity.com/paper/2405.08921/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 14 May 2024.

## Builds on (most cited first, 14 of 14)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Neural Contextual Bandits with Deep Representation and Shallow Exploration](https://mlsanity.com/paper/2012.01780/) (2020)
- [EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits](https://mlsanity.com/paper/2110.03177/) (2021)
- [Information Directed Sampling for Linear Partial Monitoring](https://mlsanity.com/paper/2002.11182/) (2020)
- [The price of bandit information in multiclass online classification](https://mlsanity.com/paper/1302.1043/) (2013)
- [Neural Active Learning with Performance Guarantees](https://mlsanity.com/paper/2106.03243/) (2021)
- [Streaming Active Learning with Deep Neural Networks](https://mlsanity.com/paper/2303.02535/) (2023)
- [Beyond Bandit Feedback in Online Multiclass Classification](https://mlsanity.com/paper/2106.03596/) (2021)
- [Linear Partial Monitoring for Sequential Decision-Making: Algorithms, Regret Bounds and Applications](https://mlsanity.com/paper/2302.03683/) (2023)
- [Improved Algorithms for Neural Active Learning](https://mlsanity.com/paper/2210.00423/) (2022)
- [Neural Active Learning Beyond Bandits](https://mlsanity.com/paper/2404.12522/) (2024)
- [Online Structured Prediction with Fenchel--Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss](https://mlsanity.com/paper/2402.08180/) (2024)
- [Active Cost-aware Labeling of Streaming Data](https://mlsanity.com/paper/2304.06808/) (2023)
- [Randomized Confidence Bounds for Stochastic Partial Monitoring](https://mlsanity.com/paper/2402.05002/) (2024)

## Built on by (most cited first, 1 of 1)

- [Instance-Dependent Regret Bounds for Nonstochastic Linear Partial Monitoring](https://mlsanity.com/paper/2510.19158/) (2025)

## Abstract (arXiv)

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 favorable performance against state-of-the-art baselines on multiple binary, multi-class and cost-sensitive OAL tasks.