# The Teaching Dimension of Linear Learners

arXiv: https://arxiv.org/abs/1512.02181 · HTML: https://mlsanity.com/paper/1512.02181/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2015.

## Built on by (most cited first, 19 of 19)

- [Towards Understanding Knowledge Distillation](https://mlsanity.com/paper/2105.13093/) (2021)
- [Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications](https://mlsanity.com/paper/1805.07687/) (2018)
- [Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints](https://mlsanity.com/paper/1906.00429/) (2019)
- [Fair Clustering Using Antidote Data](https://mlsanity.com/paper/2106.00600/) (2021)
- [Iterative Teaching by Label Synthesis](https://mlsanity.com/paper/2110.14432/) (2021)
- [Nonparametric Iterative Machine Teaching](https://mlsanity.com/paper/2306.03007/) (2023)
- [Iterative Teaching by Data Hallucination](https://mlsanity.com/paper/2210.17467/) (2022)
- [Nonparametric Teaching for Multiple Learners](https://mlsanity.com/paper/2311.10318/) (2023)
- [Learning Data Teaching Strategies Via Knowledge Tracing](https://mlsanity.com/paper/2111.07083/) (2021)
- [Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence](https://mlsanity.com/paper/2506.23908/) (2025)
- [Representational Alignment Supports Effective Machine Teaching](https://mlsanity.com/paper/2406.04302/) (2024)
- [Policy Teaching via Data Poisoning in Learning from Human Preferences](https://mlsanity.com/paper/2503.10228/) (2025)
- [Distribution Matching for Machine Teaching](https://mlsanity.com/paper/2105.13809/) (2021)
- [Black-box Generalization of Machine Teaching](https://mlsanity.com/paper/2206.15205/) (2022)
- [Training Humans to Train Robots Dynamic Motor Skills](https://mlsanity.com/paper/2104.08631/) (2021)
- [Know Thy Student: Interactive Learning with Gaussian Processes](https://mlsanity.com/paper/2204.12072/) (2022)
- [One-shot Machine Teaching: Cost Very Few Examples to Converge Faster](https://mlsanity.com/paper/2212.06416/) (2022)
- [Optimally Teaching a Linear Behavior Cloning Agent](https://mlsanity.com/paper/2311.15399/) (2023)
- [A Machine Learning Theory Perspective on Strategic Litigation](https://mlsanity.com/paper/2506.03411/) (2025)

## Abstract (arXiv)

Teaching dimension is a learning theoretic quantity that specifies the minimum training set size to teach a target model to a learner. Previous studies on teaching dimension focused on version-space learners which maintain all hypotheses consistent with the training data, and cannot be applied to modern machine learners which select a specific hypothesis via optimization. This paper presents the first known teaching dimension for ridge regression, support vector machines, and logistic regression. We also exhibit optimal training sets that match these teaching dimensions. Our approach generalizes to other linear learners.