# Machine Teaching for Bayesian Learners in the Exponential Family

arXiv: https://arxiv.org/abs/1306.4947 · HTML: https://mlsanity.com/paper/1306.4947/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2013.

## Built on by (most cited first, 18 of 18)

- [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)
- [Iterative Teacher-Aware Learning](https://mlsanity.com/paper/2110.00137/) (2021)
- [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)
- [ML-Based Teaching Systems: A Conceptual Framework](https://mlsanity.com/paper/2305.07681/) (2023)
- [Distribution Matching for Machine Teaching](https://mlsanity.com/paper/2105.13809/) (2021)
- [MAP- and MLE-Based Teaching](https://mlsanity.com/paper/2307.05252/) (2023)
- [Utility-based Adaptive Teaching Strategies using Bayesian Theory of Mind](https://mlsanity.com/paper/2309.17275/) (2023)
- [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)
- [Manipulating Predictions over Discrete Inputs in Machine Teaching](https://mlsanity.com/paper/2401.17865/) (2024)
- [Stealthy Poisoning Attacks Bypass Defenses in Regression Settings](https://mlsanity.com/paper/2601.22308/) (2026)

## Abstract (arXiv)

What if there is a teacher who knows the learning goal and wants to design good training data for a machine learner? We propose an optimal teaching framework aimed at learners who employ Bayesian models. Our framework is expressed as an optimization problem over teaching examples that balance the future loss of the learner and the effort of the teacher. This optimization problem is in general hard. In the case where the learner employs conjugate exponential family models, we present an approximate algorithm for finding the optimal teaching set. Our algorithm optimizes the aggregate sufficient statistics, then unpacks them into actual teaching examples. We give several examples to illustrate our framework.