Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Nonparametric Iterative Machine Teaching
Authors: Chen Zhang, Xiaofeng Cao, Weiyang Liu, Ivor Tsang, James Kwok
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we verify the correctness of our theoretical findings with extensive experiments in nonparametric scenarios. We test our RFT and GFT on both synthetic and real-world data, on which we find these two algorithms present satisfactory capability to tackle nonparametric teaching tasks. |
| Researcher Affiliation | Academia | 1School of Artificial Intelligence, Jilin University, China 2Max Planck Institute for Intelligent Systems, T ubingen, Germany 3University of Cambridge, United Kingdom 4Centre for Frontier AI Research and Institute of High Performance Computing, A*STAR, Singapore 5Hong Kong University of Science and Technology. |
| Pseudocode | Yes | Algorithm 1 Random / Greedy Functional Teaching |
| Open Source Code | Yes | Our source code is available at https://github.com/chen2hang/Nonparametric Teaching. |
| Open Datasets | Yes | Consider a digit (MNIST (Le Cun, 1998)) teaching instance, one can image a digit figure as a surface in 3D space... EMNIST from (Cohen et al., 2017)... we pick two facial figures form the ORL database (http://www.cam-orl.co.uk) |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits needed to reproduce the experiment. While it mentions using MNIST (both training and testing sets), it does not specify the split percentages or counts. |
| Hardware Specification | Yes | Our implementation is based on Intel(R) Core(TM) i7-8750H and NVIDIA GTX 1050 Ti with Max-Q Design. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., specific library versions or solver versions). |
| Experiment Setup | Yes | For this regression problem, we assume the loss function of the learner is square loss L = (y - f(x))^2... The learning rate ηt is fixed as 0.01. ... We set kernel as the popular and general RBF K(x, x') = exp(-||x - x'||^2 / (2 * sigma^2)). |