# An Application of Online Learning to Spacecraft Memory Dump Optimization

arXiv: https://arxiv.org/abs/2202.06617 · HTML: https://mlsanity.com/paper/2202.06617/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 26 references that are in the map.
- First posted on arXiv 14 Feb 2022.

## Builds on (most cited first, 12 of 12)

- [Bilateral Trade: A Regret Minimization Perspective](https://mlsanity.com/paper/2109.12974/) (2021)
- [Dynamic Pricing with Finitely Many Unknown Valuations](https://mlsanity.com/paper/1807.03288/) (2018)
- [Cooperative Online Learning: Keeping your Neighbors Updated](https://mlsanity.com/paper/1901.08082/) (2019)
- [A Regret Analysis of Bilateral Trade](https://mlsanity.com/paper/2102.08754/) (2021)
- [Nonstochastic Bandits with Composite Anonymous Feedback](https://mlsanity.com/paper/2112.02866/) (2021)
- [Regret analysis of the Piyavskii-Shubert algorithm for global Lipschitz optimization](https://mlsanity.com/paper/2002.02390/) (2020)
- [A Nearest Neighbor Characterization of Lebesgue Points in Metric Measure Spaces](https://mlsanity.com/paper/2007.03937/) (2020)
- [Instance-Dependent Bounds for Zeroth-order Lipschitz Optimization with Error Certificates](https://mlsanity.com/paper/2102.01977/) (2021)
- [An Efficient Algorithm for Cooperative Semi-Bandits](https://mlsanity.com/paper/2010.01818/) (2020)
- [The sample complexity of level set approximation](https://mlsanity.com/paper/2010.13405/) (2020)
- [Cooperative Online Learning with Feedback Graphs](https://mlsanity.com/paper/2106.04982/) (2021)
- [ROI Maximization in Stochastic Online Decision-Making](https://mlsanity.com/paper/1905.11797/) (2019)

## Abstract (arXiv)

In this paper, we present a real-world application of online learning with expert advice to the field of Space Operations, testing our theory on real-life data coming from the Copernicus Sentinel-6 satellite. We show that in Spacecraft Memory Dump Optimization, a lightweight Follow-The-Leader algorithm leads to an increase in performance of over 60% when compared to traditional techniques.