# Online Resource Allocation with Convex-set Machine-Learned Advice

arXiv: https://arxiv.org/abs/2306.12282 · HTML: https://mlsanity.com/paper/2306.12282/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 21 Jun 2023.

## Builds on (most cited first, 7 of 7)

- [Competitive caching with machine learned advice](https://mlsanity.com/paper/1802.05399/) (2018)
- [Data-Driven Robust Optimization](https://mlsanity.com/paper/1401.0212/) (2013)
- [Near-Optimal Bounds for Online Caching with Machine Learned Advice](https://mlsanity.com/paper/1910.12172/) (2019)
- [Online Allocation with Traffic Spikes: Mixing Adversarial and Stochastic Models](https://mlsanity.com/paper/1711.05764/) (2017)
- [Online Resource Allocation under Partially Predictable Demand](https://mlsanity.com/paper/1810.00447/) (2018)
- [On Policies for Single-leg Revenue Management with Limited Demand Information](https://mlsanity.com/paper/1810.10900/) (2018)
- [Online Resource Allocation with Samples](https://mlsanity.com/paper/2210.04774/) (2022)

## Built on by (most cited first, 7 of 7)

- [Adaptively Robust LLM Inference Optimization under Prediction Uncertainty](https://mlsanity.com/paper/2508.14544/) (2025)
- [Online Fair Division with Additional Information](https://mlsanity.com/paper/2505.24503/) (2025)
- [Learning in Repeated Multi-Unit Pay-As-Bid Auctions](https://mlsanity.com/paper/2307.15193/) (2023)
- [Best of Many in Both Worlds: Online Resource Allocation with Predictions under Unknown Arrival Model](https://mlsanity.com/paper/2402.13530/) (2024)
- [Efficiently Solving Discounted MDPs with Predictions on Transition Matrices](https://mlsanity.com/paper/2502.15345/) (2025)
- [Constrained Online Two-stage Stochastic Optimization: Algorithm with (and without) Predictions](https://mlsanity.com/paper/2401.01077/) (2024)
- [Learning-Augmented Algorithms for Online Concave Packing and Convex Covering Problems](https://mlsanity.com/paper/2411.08332/) (2024)

## Abstract (arXiv)

Decision-makers often have access to machine-learned predictions about future demand that can help guide online resource allocation decisions. However, such predictions may be inaccurate. We develop a framework for online resource allocation with potentially unreliable machine-learned advice, where the advice is represented as a convex uncertainty set for the demand vector rather than a single point estimate. We introduce a parameterized class of Pareto-optimal online algorithms that balance consistency and robustness. The consistent ratio measures performance when the advice is accurate, while the robust ratio measures performance under adversarial demand when the advice is inaccurate. For a target consistency level C, our algorithms maximize robustness subject to achieving at least consistency level C. Our approach extends classical protection-level algorithms by introducing adaptive protection levels that dynamically respond to uncertainty in the advice. We also provide a method for computing the maximum achievable consistency level. Numerical experiments demonstrate that our algorithms outperform benchmark methods, including approaches based solely on point forecasts, by effectively balancing worst-case and average-case performance.