# Multi-Task Combinatorial Bandits for Budget Allocation

arXiv: https://arxiv.org/abs/2409.00561 · HTML: https://mlsanity.com/paper/2409.00561/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 41 references that are in the map.
- First posted on arXiv 31 Aug 2024.

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

- [Neural Tangent Kernel: Convergence and Generalization in Neural Networks](https://mlsanity.com/paper/1806.07572/) (2018)
- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [Bandits with Knapsacks](https://mlsanity.com/paper/1305.2545/) (2013)
- [Neural Thompson Sampling](https://mlsanity.com/paper/2010.00827/) (2020)
- [Bandits with concave rewards and convex knapsacks](https://mlsanity.com/paper/1402.5758/) (2014)
- [Linear Contextual Bandits with Knapsacks](https://mlsanity.com/paper/1507.06738/) (2015)
- [Optimized Cost per Click in Taobao Display Advertising](https://mlsanity.com/paper/1703.02091/) (2017)
- [Sample-Then-Optimize Batch Neural Thompson Sampling](https://mlsanity.com/paper/2210.06850/) (2022)
- [Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models](https://mlsanity.com/paper/2108.06422/) (2021)
- [Combinatorial Semi-Bandits with Knapsacks](https://mlsanity.com/paper/1705.08110/) (2017)
- [Online Joint Bid/Daily Budget Optimization of Internet Advertising Campaigns](https://mlsanity.com/paper/2003.01452/) (2020)
- [Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework](https://mlsanity.com/paper/2202.13227/) (2022)
- [Combinatorial Multi-armed Bandits for Resource Allocation](https://mlsanity.com/paper/2105.04373/) (2021)
- [Cross-channel Budget Coordination for Online Advertising System](https://mlsanity.com/paper/2305.06883/) (2023)

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

- [CoBA-RL: Capability-Oriented Budget Allocation for Reinforcement Learning in LLMs](https://mlsanity.com/paper/2602.03048/) (2026)
- [Combinatorial Rising Bandits](https://mlsanity.com/paper/2412.00798/) (2024)

## Abstract (arXiv)

Today's top advertisers typically manage hundreds of campaigns simultaneously and consistently launch new ones throughout the year. A crucial challenge for marketing managers is determining the optimal allocation of limited budgets across various ad lines in each campaign to maximize cumulative returns, especially given the huge uncertainty in return outcomes. In this paper, we propose to formulate budget allocation as a multi-task combinatorial bandit problem and introduce a novel online budget allocation system. The proposed system: i) integrates a Bayesian hierarchical model to intelligently utilize the metadata of campaigns and ad lines and budget size, ensuring efficient information sharing; ii) provides the flexibility to incorporate diverse modeling techniques such as Linear Regression, Gaussian Processes, and Neural Networks, catering to diverse environmental complexities; and iii) employs the Thompson sampling (TS) technique to strike a balance between exploration and exploitation. Through offline evaluation and online experiments, our system demonstrates robustness and adaptability, effectively maximizing the overall cumulative returns. A Python implementation of the proposed procedure is available at https://anonymous.4open.science/r/MCMAB.