# Learning Multiclass Classifier Under Noisy Bandit Feedback

arXiv: https://arxiv.org/abs/2006.03545 · HTML: https://mlsanity.com/paper/2006.03545/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [Delaytron: Efficient Learning of Multiclass Classifiers with Delayed Bandit Feedbacks](https://mlsanity.com/paper/2205.08234/) (2022)
- [Multi-Action Dialog Policy Learning from Logged User Feedback](https://mlsanity.com/paper/2302.13505/) (2023)

## Abstract (arXiv)

This paper addresses the problem of multiclass classification with corrupted or noisy bandit feedback. In this setting, the learner may not receive true feedback. Instead, it receives feedback that has been flipped with some non-zero probability. We propose a novel approach to deal with noisy bandit feedback based on the unbiased estimator technique. We further offer a method that can efficiently estimate the noise rates, thus providing an end-to-end framework. The proposed algorithm enjoys a mistake bound of the order of O(√T) in the high noise case and of the order of O(T^nicefrac23) in the worst case. We show our approach's effectiveness using extensive experiments on several benchmark datasets.