# Teacher Improves Learning by Selecting a Training Subset

arXiv: https://arxiv.org/abs/1802.08946 · HTML: https://mlsanity.com/paper/1802.08946/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2018.

## Built on by (most cited first, 6 of 6)

- [Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning](https://mlsanity.com/paper/2406.06207/) (2024)
- [Representational Alignment Supports Effective Machine Teaching](https://mlsanity.com/paper/2406.04302/) (2024)
- [Data Selection for ERMs](https://mlsanity.com/paper/2504.14572/) (2025)
- [Feedback Coding for Active Learning](https://mlsanity.com/paper/2103.00654/) (2021)
- [Time-Constrained Learning](https://mlsanity.com/paper/2202.01913/) (2022)
- [Optimally Teaching a Linear Behavior Cloning Agent](https://mlsanity.com/paper/2311.15399/) (2023)

## Abstract (arXiv)

We call a learner super-teachable if a teacher can trim down an iid training set while making the learner learn even better. We provide sharp super-teaching guarantees on two learners: the maximum likelihood estimator for the mean of a Gaussian, and the large margin classifier in 1D. For general learners, we provide a mixed-integer nonlinear programming-based algorithm to find a super teaching set. Empirical experiments show that our algorithm is able to find good super-teaching sets for both regression and classification problems.