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 [1].
On Batch Teaching Without Collusion
Authors: Shaun Fallat, David Kirkpatrick, Hans U. Simon, Abolghasem Soltani, Sandra Zilles
JMLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Formal models of learning from teachers need to respect certain criteria to avoid collusion. The paper at hand is concerned with abstract notions of teaching, as studied in computational learning theory. This paper introduces a new model of teaching, called no-clash teaching, together with the corresponding parameter NCTD(C). We also study a corresponding notion NCTD+ for the case of learning from positive data only, establish useful bounds on NCTD and NCTD+, and discuss relations of these parameters to other complexity parameters of interest in computational learning theory. |
| Researcher Affiliation | Academia | Shaun Fallat Department of Mathematics and Statistics, University of Regina; David Kirkpatrick Department of Computer Science, University of British Columbia; Hans U. Simon Max Planck Institute for Informatics; Abolghasem Soltani Department of Computer Science, University of Regina; Sandra Zilles Department of Computer Science, University of Regina. |
| Pseudocode | No | No pseudocode or algorithm blocks are present in the paper. The paper focuses on theoretical definitions, theorems, and proofs. |
| Open Source Code | No | No explicit statement or link regarding open-source code for the methodology described in this paper is found. |
| Open Datasets | No | The paper discusses abstract concept classes and uses theoretical examples such as 'the powerset over the domain {x1, . . . , xm}' and 'Sylvester-Hadamard matrices,' which are mathematical constructs rather than open datasets with access information. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments requiring dataset splits. No information on dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental hardware. No hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or version numbers for experimental replication. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or training configurations. |