Preference-based Teaching
Authors: Ziyuan Gao, Christoph Ries, Hans U. Simon, Sandra Zilles
JMLR 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce a new model of teaching named preference-based teaching and a corresponding complexity parameter the preference-based teaching dimension (PBTD) representing the worstcase number of examples needed to teach any concept in a given concept class. On top of presenting these concrete results, we provide the reader with a theoretical framework (of a combinatorial flavor) which helps to derive bounds on the PBTD. |
| Researcher Affiliation | Academia | Ziyuan Gao EMAIL Department of Computer Science, University of Regina Christoph Ries EMAIL Department of Mathematics, Ruhr-University Bochum Hans U. Simon EMAIL Department of Mathematics, Ruhr-University Bochum Sandra Zilles EMAIL Department of Computer Science, University of Regina |
| Pseudocode | No | The paper focuses on theoretical models, definitions, lemmas, and theorems. It does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper cites an arXiv e-print (Gao et al., 2017) and a previous conference version (Gao et al., 2016). However, it does not contain any explicit statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper introduces a theoretical model and analyzes various concept classes (e.g., closed sets, linear sets, half-spaces) mathematically. It does not perform empirical evaluations using specific datasets, and thus, no dataset access information is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets, so there is no mention of training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not report on any computational experiments. Consequently, no hardware specifications are provided. |
| Software Dependencies | No | The paper focuses on theoretical aspects and does not involve computational experiments, thus no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not detail any empirical experiments. Therefore, there are no descriptions of experimental setup, hyperparameters, or training configurations. |