# Toward a general, scaleable framework for Bayesian teaching with applications to topic models

arXiv: https://arxiv.org/abs/1605.07999 · HTML: https://mlsanity.com/paper/1605.07999/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2016.

## Built on by (most cited first, 4 of 4)

- [Iterative Teaching by Label Synthesis](https://mlsanity.com/paper/2110.14432/) (2021)
- [Explainable AI for medical imaging: Explaining pneumothorax diagnoses with Bayesian Teaching](https://mlsanity.com/paper/2106.04684/) (2021)
- [Explainable AI for Natural Adversarial Images](https://mlsanity.com/paper/2106.09106/) (2021)
- [MAP- and MLE-Based Teaching](https://mlsanity.com/paper/2307.05252/) (2023)

## Abstract (arXiv)

Machines, not humans, are the world's dominant knowledge accumulators but humans remain the dominant decision makers. Interpreting and disseminating the knowledge accumulated by machines requires expertise, time, and is prone to failure. The problem of how best to convey accumulated knowledge from computers to humans is a critical bottleneck in the broader application of machine learning. We propose an approach based on human teaching where the problem is formalized as selecting a small subset of the data that will, with high probability, lead the human user to the correct inference. This approach, though successful for modeling human learning in simple laboratory experiments, has failed to achieve broader relevance due to challenges in formulating general and scalable algorithms. We propose general-purpose teaching via pseudo-marginal sampling and demonstrate the algorithm by teaching topic models. Simulation results show our sampling-based approach: effectively approximates the probability where ground-truth is possible via enumeration, results in data that are markedly different from those expected by random sampling, and speeds learning especially for small amounts of data. Application to movie synopsis data illustrates differences between teaching and random sampling for teaching distributions and specific topics, and demonstrates gains in scalability and applicability to real-world problems.