mlsanityData to 7 Oct 2026

Iterative Machine Teaching

arXiv 1705.10470 ↗First posted 30 May 2017stat.ML cs.LGcited by 40

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Abstract (arXiv)

In this paper, we consider the problem of machine teaching, the inverse problem of machine learning. Different from traditional machine teaching which views the learners as batch algorithms, we study a new paradigm where the learner uses an iterative algorithm and a teacher can feed examples sequentially and intelligently based on the current performance of the learner. We show that the teaching complexity in the iterative case is very different from that in the batch case. Instead of constructing a minimal training set for learners, our iterative machine teaching focuses on achieving fast convergence in the learner model. Depending on the level of information the teacher has from the learner model, we design teaching algorithms which can provably reduce the number of teaching examples and achieve faster convergence than learning without teachers. We also validate our theoretical findings with extensive experiments on different data distribution and real image datasets.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 May 2017.

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Citing papers per year: 2019: 1; 2020: 1; 2021: 10; 2022: 7; 2023: 8; 2024: 6; 2025: 5; 2026: 2.

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  1. 2023MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Modelscited by 724
  2. 2024Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervisioncited by 95
  3. 2022Eliciting and Learning with Soft Labels from Every Annotatorcited by 43
  4. 2019Interactive Teaching Algorithms for Inverse Reinforcement Learningcited by 24
  5. 2021Breaking the Linear Iteration Cost Barrier for Some Well-known Conditional Gradient Methods Using MaxIP Data-structurescited by 19
  6. 2023Proximal Curriculum for Reinforcement Learning Agentscited by 19
  7. 2024Nonparametric Teaching of Implicit Neural Representationscited by 13
  8. 2021Teaching an Active Learner with Contrastive Examplescited by 12
  9. 2021Iterative Teaching by Label Synthesiscited by 10
  10. 2025Reward Model Overoptimisation in Iterated RLHFcited by 10
  11. 2021Curriculum Design for Teaching via Demonstrations: Theory and Applicationscited by 9
  12. 2023Nonparametric Iterative Machine Teachingcited by 9
28 more
  1. 2022Iterative Teaching by Data Hallucinationcited by 8
  2. 2022Human-in-the-Loop Mixupcited by 8
  3. 2022Perspectives on Incorporating Expert Feedback into Model Updatescited by 7
  4. 2023Nonparametric Teaching for Multiple Learnerscited by 7
  5. 2021Iterative Teacher-Aware Learningcited by 6
  6. 2021Learning Data Teaching Strategies Via Knowledge Tracingcited by 6
  7. 2024Proximal Curriculum with Task Correlations for Deep Reinforcement Learningcited by 6
  8. 2025Beyond Statistical Learning: Exact Learning Is Essential for General Intelligencecited by 6
  9. 2021A Student-Teacher Architecture for Dialog Domain Adaptation under the Meta-Learning Settingcited by 5
  10. 2025Nonparametric Teaching for Graph Property Learnerscited by 5
  11. 2021Agent Smith: Teaching Question Answering to Jill Watsoncited by 4
  12. 2025Model Merging with Functional Dual Anchorscited by 4
  13. 2024Representational Alignment Supports Effective Machine Teachingcited by 3
  14. 2022Absolute Zero-Shot Learningcited by 2
  15. 2023L2T-DLN: Learning to Teach with Dynamic Loss Networkcited by 2
  16. 2024L-WISE: Boosting Human Visual Category Learning Through Model-Based Image Selection and Enhancementcited by 2
  17. 2025Review of Hallucination Understanding in Large Language and Vision Modelscited by 2
  18. 2022Human-AI Interaction Design in Machine Teachingcited by 1
  19. 2023Causative Cyberattacks on Online Learning-based Automated Demand Response Systemscited by 1
  20. 2026Nonparametric Teaching of Attention Learnerscited by 1
  21. 2020Interaction-limited Inverse Reinforcement Learningcited by 0
  22. 2021Towards Scheduling Federated Deep Learning using Meta-Gradients for Inter-Hospital Learningcited by 0
  23. 2021Conditional Teaching Sizecited by 0
  24. 2022One-shot Machine Teaching: Cost Very Few Examples to Converge Fastercited by 0
  25. 2023Attacks on Online Learners: a Teacher-Student Analysiscited by 0
  26. 2023Interactively Teaching an Inverse Reinforcement Learner with Limited Feedbackcited by 0
  27. 2024Manipulating Predictions over Discrete Inputs in Machine Teachingcited by 0
  28. 2026Multi-Modal, Multi-Environment Machine Teaching for Robust Reward Learningcited by 0