# Towards Black-box Iterative Machine Teaching

arXiv: https://arxiv.org/abs/1710.07742 · HTML: https://mlsanity.com/paper/1710.07742/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2017.

## Built on by (most cited first, 20 of 20)

- [Interactive Teaching Algorithms for Inverse Reinforcement Learning](https://mlsanity.com/paper/1905.11867/) (2019)
- [Breaking the Linear Iteration Cost Barrier for Some Well-known Conditional Gradient Methods Using MaxIP Data-structures](https://mlsanity.com/paper/2111.15139/) (2021)
- [Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints](https://mlsanity.com/paper/1906.00429/) (2019)
- [Nonparametric Teaching of Implicit Neural Representations](https://mlsanity.com/paper/2405.10531/) (2024)
- [Teaching an Active Learner with Contrastive Examples](https://mlsanity.com/paper/2110.14888/) (2021)
- [Iterative Teaching by Label Synthesis](https://mlsanity.com/paper/2110.14432/) (2021)
- [Curriculum Design for Teaching via Demonstrations: Theory and Applications](https://mlsanity.com/paper/2106.04696/) (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)
- [Nonparametric Teaching for Graph Property Learners](https://mlsanity.com/paper/2505.14170/) (2025)
- [Representational Alignment Supports Effective Machine Teaching](https://mlsanity.com/paper/2406.04302/) (2024)
- [Distribution Matching for Machine Teaching](https://mlsanity.com/paper/2105.13809/) (2021)
- [Time-Constrained Learning](https://mlsanity.com/paper/2202.01913/) (2022)
- [Black-box Generalization of Machine Teaching](https://mlsanity.com/paper/2206.15205/) (2022)
- [Nonparametric Teaching of Attention Learners](https://mlsanity.com/paper/2602.20461/) (2026)
- [One-shot Machine Teaching: Cost Very Few Examples to Converge Faster](https://mlsanity.com/paper/2212.06416/) (2022)
- [Interactively Teaching an Inverse Reinforcement Learner with Limited Feedback](https://mlsanity.com/paper/2309.09095/) (2023)
- [From Human Corrections to Learned Segmentations: A Critical Mathematical Review of Information, Learning, and Human Effort](https://mlsanity.com/paper/2610.20396/) (2026)

## Abstract (arXiv)

In this paper, we make an important step towards the black-box machine teaching by considering the cross-space machine teaching, where the teacher and the learner use different feature representations and the teacher can not fully observe the learner's model. In such scenario, we study how the teacher is still able to teach the learner to achieve faster convergence rate than the traditional passive learning. We propose an active teacher model that can actively query the learner (i.e., make the learner take exams) for estimating the learner's status and provably guide the learner to achieve faster convergence. The sample complexities for both teaching and query are provided. In the experiments, we compare the proposed active teacher with the omniscient teacher and verify the effectiveness of the active teacher model.