Non-Cheating Teaching Revisited: A New Probabilistic Machine Teaching Model
Authors: Cèsar Ferri, José Hernández-Orallo, Jan Arne Telle
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Overall, this new setting is more general than the traditional machine teaching models, yet at the same time more intuitively capturing a less abrupt notion of non-cheating teaching. We show a simple procedure that builds the witness joint distribution from the ground joint distribution. We prove a chain of relations, also with a theoretical lower bound, on the teaching dimension of the old and new models. |
| Researcher Affiliation | Academia | C esar Ferri 1 , Jos e Hern andez-Orallo 1 , Jan Arne Telle 2 1 VRAIN, Universitat Polit ecnica de Val encia, Spain. 2 Department of Informatics, University of Bergen, Norway. |
| Pseudocode | No | The paper contains no pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing source code or links to a code repository. |
| Open Datasets | No | This is a theoretical paper and does not involve training on datasets. |
| Dataset Splits | No | This is a theoretical paper and does not involve dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |