mlsanityData to 7 Oct 2026

Machine Teaching for Bayesian Learners in the Exponential Family

arXiv 1306.4947 ↗First posted 20 Jun 2013cs.LGcited by 18

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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.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2013.

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Citing papers per year: 2018: 1; 2019: 1; 2020: 0; 2021: 6; 2022: 2; 2023: 6; 2024: 1; 2025: 0; 2026: 1.

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  1. 2021Towards Understanding Knowledge Distillationcited by 158
  2. 2018Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applicationscited by 28
  3. 2019Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraintscited by 18
  4. 2021Fair Clustering Using Antidote Datacited by 10
  5. 2021Iterative Teaching by Label Synthesiscited by 10
  6. 2023Nonparametric Iterative Machine Teachingcited by 9
  7. 2022Iterative Teaching by Data Hallucinationcited by 8
  8. 2023Nonparametric Teaching for Multiple Learnerscited by 7
  9. 2021Iterative Teacher-Aware Learningcited by 6
  10. 2021Learning Data Teaching Strategies Via Knowledge Tracingcited by 6
  11. 2023ML-Based Teaching Systems: A Conceptual Frameworkcited by 3
  12. 2021Distribution Matching for Machine Teachingcited by 1
6 more
  1. 2023MAP- and MLE-Based Teachingcited by 1
  2. 2023Utility-based Adaptive Teaching Strategies using Bayesian Theory of Mindcited by 1
  3. 2022One-shot Machine Teaching: Cost Very Few Examples to Converge Fastercited by 0
  4. 2023Optimally Teaching a Linear Behavior Cloning Agentcited by 0
  5. 2024Manipulating Predictions over Discrete Inputs in Machine Teachingcited by 0
  6. 2026Stealthy Poisoning Attacks Bypass Defenses in Regression Settingscited by 0