# Learning with Abandonment

arXiv: https://arxiv.org/abs/1802.08718 · HTML: https://mlsanity.com/paper/1802.08718/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2018.

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

- [Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment](https://mlsanity.com/paper/2205.13566/) (2022)
- [Sequential Choice Bandits with Feedback for Personalizing users' experience](https://mlsanity.com/paper/2101.01572/) (2021)

## Abstract (arXiv)

Consider a platform that wants to learn a personalized policy for each user, but the platform faces the risk of a user abandoning the platform if she is dissatisfied with the actions of the platform. For example, a platform is interested in personalizing the number of newsletters it sends, but faces the risk that the user unsubscribes forever. We propose a general thresholded learning model for scenarios like this, and discuss the structure of optimal policies. We describe salient features of optimal personalization algorithms and how feedback the platform receives impacts the results. Furthermore, we investigate how the platform can efficiently learn the heterogeneity across users by interacting with a population and provide performance guarantees.