# Finite sample learning of moving targets

arXiv: https://arxiv.org/abs/2408.04406 · HTML: https://mlsanity.com/paper/2408.04406/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 8 Aug 2024.

## Builds on (most cited first, 5 of 5)

- [On the Sample Complexity of the Linear Quadratic Regulator](https://mlsanity.com/paper/1710.01688/) (2017)
- [Compression, Generalization and Learning](https://mlsanity.com/paper/2301.12767/) (2023)
- [On the exact feasibility of convex scenario programs with discarded constraints](https://mlsanity.com/paper/2003.13433/) (2020)
- [Learning with a Drifting Target Concept](https://mlsanity.com/paper/1505.05215/) (2015)
- [Probably Approximately Correct Nash Equilibrium Learning](https://mlsanity.com/paper/1903.10387/) (2019)

## Built on by (most cited first, 1 of 1)

- [SPoRt -- Safe Policy Ratio: Certified Training and Deployment of Task Policies in Model-Free RL](https://mlsanity.com/paper/2504.06386/) (2025)

## Abstract (arXiv)

We consider a moving target that we seek to learn from samples. Our results extend randomized techniques developed in control and optimization for a constant target to the case where the target is changing. We derive a novel bound on the number of samples that are required to construct a probably approximately correct (PAC) estimate of the target. Furthermore, when the moving target is a convex polytope, we provide a constructive method of generating the PAC estimate using a mixed integer linear program (MILP). The proposed method is demonstrated on an application to autonomous emergency braking.