# Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback

arXiv: https://arxiv.org/abs/1909.01504 · HTML: https://mlsanity.com/paper/1909.01504/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2019.

## Built on by (most cited first, 13 of 13)

- [Combinatorial Multi-armed Bandits for Resource Allocation](https://mlsanity.com/paper/2105.04373/) (2021)
- [Bayesian Optimization under Stochastic Delayed Feedback](https://mlsanity.com/paper/2206.09341/) (2022)
- [Censored Semi-Bandits for Resource Allocation](https://mlsanity.com/paper/2104.05781/) (2021)
- [Keep Everyone Happy: Online Fair Division of Numerous Items with Few Copies](https://mlsanity.com/paper/2408.12845/) (2024)
- [Ranked Prioritization of Groups in Combinatorial Bandit Allocation](https://mlsanity.com/paper/2205.05659/) (2022)
- [Learning Thresholds with Latent Values and Censored Feedback](https://mlsanity.com/paper/2312.04653/) (2023)
- [BORA: Bayesian Optimization for Resource Allocation](https://mlsanity.com/paper/2210.05977/) (2022)
- [Allocating Divisible Resources on Arms with Unknown and Random Rewards](https://mlsanity.com/paper/2306.16578/) (2023)
- [Master-slave Deep Architecture for Top-K Multi-armed Bandits with Non-linear Bandit Feedback and Diversity Constraints](https://mlsanity.com/paper/2308.12680/) (2023)
- [Bandits in Flux: Adversarial Constraints in Dynamic Environments](https://mlsanity.com/paper/2601.19867/) (2026)
- [Cost of Structural Learning Under Censored Feedback: A Threshold-Bandit Approach](https://mlsanity.com/paper/2605.27076/) (2026)
- [Nonparametric Contextual Pricing and Inventory Learning under Censored Demand](https://mlsanity.com/paper/2608.30944/) (2026)
- [Strategic Classification Has a Missing Lever: Audit Risk](https://mlsanity.com/paper/2609.22534/) (2026)

## Abstract (arXiv)

In this paper, we study censored Semi-Bandits, a novel variant of the semi-bandits problem. The learner is assumed to have a fixed amount of resources, which it allocates to the arms at each time step. The loss observed from an arm is random and depends on the amount of resources allocated to it. More specifically, the loss equals zero if the allocation for the arm exceeds a constant (but unknown)threshold that can be dependent on the arm. Our goal is to learn a feasible allocation that minimizes the expected loss. The problem is challenging because the loss distribution and threshold value of each arm are unknown. We study this novel setting by establishing its `equivalence' to Multiple-Play Multi-Armed Bandits(MP-MAB) and Combinatorial Semi-Bandits. Exploiting these equivalences, we derive optimal algorithms for our setting using existing algorithms for MP-MABand Combinatorial Semi-Bandits. Experiments on synthetically generated data validate performance guarantees of the proposed algorithms.