# Nonparametric Teaching for Multiple Learners

arXiv: https://arxiv.org/abs/2311.10318 · HTML: https://mlsanity.com/paper/2311.10318/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 83 references that are in the map.
- First posted on arXiv 17 Nov 2023.

## Builds on (most cited first, 30 of 38)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models](https://mlsanity.com/paper/2309.12284/) (2023)
- [Train faster, generalize better: Stability of stochastic gradient descent](https://mlsanity.com/paper/1509.01240/) (2015)
- [Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm](https://mlsanity.com/paper/1608.04471/) (2016)
- [From data to functa: Your data point is a function and you can treat it like one](https://mlsanity.com/paper/2201.12204/) (2022)
- [Stein Variational Gradient Descent as Gradient Flow](https://mlsanity.com/paper/1704.07520/) (2017)
- [Maximum Mean Discrepancy Gradient Flow](https://mlsanity.com/paper/1906.04370/) (2019)
- [Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning](https://mlsanity.com/paper/2003.12909/) (2020)
- [Policy Poisoning in Batch Reinforcement Learning and Control](https://mlsanity.com/paper/1910.05821/) (2019)
- [An Overview of Machine Teaching](https://mlsanity.com/paper/1801.05927/) (2018)
- [Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework](https://mlsanity.com/paper/2002.09169/) (2020)
- [Iterative Machine Teaching](https://mlsanity.com/paper/1705.10470/) (2017)
- [Near-Optimally Teaching the Crowd to Classify](https://mlsanity.com/paper/1402.2092/) (2014)
- [Fast Discrete Distribution Clustering Using Wasserstein Barycenter with Sparse Support](https://mlsanity.com/paper/1510.00012/) (2015)
- [Stochastic Wasserstein Barycenters](https://mlsanity.com/paper/1802.05757/) (2018)
- [Interactive Teaching Algorithms for Inverse Reinforcement Learning](https://mlsanity.com/paper/1905.11867/) (2019)
- [Towards Black-box Iterative Machine Teaching](https://mlsanity.com/paper/1710.07742/) (2017)
- [The Teaching Dimension of Linear Learners](https://mlsanity.com/paper/1512.02181/) (2015)
- [Machine Teaching for Bayesian Learners in the Exponential Family](https://mlsanity.com/paper/1306.4947/) (2013)
- [Teaching Multiple Concepts to a Forgetful Learner](https://mlsanity.com/paper/1805.08322/) (2018)
- [Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners](https://mlsanity.com/paper/1802.05190/) (2018)
- [Functional Gradient Boosting based on Residual Network Perception](https://mlsanity.com/paper/1802.09031/) (2018)
- [Convex Learning of Multiple Tasks and their Structure](https://mlsanity.com/paper/1504.03101/) (2015)
- [Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models](https://mlsanity.com/paper/1910.10944/) (2019)
- [Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory Learners](https://mlsanity.com/paper/1804.06481/) (2018)
- [Machine Teaching of Active Sequential Learners](https://mlsanity.com/paper/1809.02869/) (2018)
- [An Optimal Control Approach to Sequential Machine Teaching](https://mlsanity.com/paper/1810.06175/) (2018)

## Built on by (most cited first, 7 of 7)

- [Nonparametric Teaching of Implicit Neural Representations](https://mlsanity.com/paper/2405.10531/) (2024)
- [Imaging Interiors: An Implicit Solution to Electromagnetic Inverse Scattering Problems](https://mlsanity.com/paper/2407.09352/) (2024)
- [Nonparametric Teaching for Graph Property Learners](https://mlsanity.com/paper/2505.14170/) (2025)
- [NTK-Guided Implicit Neural Teaching](https://mlsanity.com/paper/2511.15487/) (2025)
- [Nonparametric Teaching of Attention Learners](https://mlsanity.com/paper/2602.20461/) (2026)
- [Fighter: Unveiling the Graph Convolutional Nature of Transformers in Time Series Modeling](https://mlsanity.com/paper/2510.17106/) (2025)
- [Model Evolution Under Zeroth-Order Optimization: A Neural Tangent Kernel Perspective](https://mlsanity.com/paper/2603.21169/) (2026)

## Abstract (arXiv)

We study the problem of teaching multiple learners simultaneously in the nonparametric iterative teaching setting, where the teacher iteratively provides examples to the learner for accelerating the acquisition of a target concept. This problem is motivated by the gap between current single-learner teaching setting and the real-world scenario of human instruction where a teacher typically imparts knowledge to multiple students. Under the new problem formulation, we introduce a novel framework -- Multi-learner Nonparametric Teaching (MINT). In MINT, the teacher aims to instruct multiple learners, with each learner focusing on learning a scalar-valued target model. To achieve this, we frame the problem as teaching a vector-valued target model and extend the target model space from a scalar-valued reproducing kernel Hilbert space used in single-learner scenarios to a vector-valued space. Furthermore, we demonstrate that MINT offers significant teaching speed-up over repeated single-learner teaching, particularly when the multiple learners can communicate with each other. Lastly, we conduct extensive experiments to validate the practicality and efficiency of MINT.