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..
On Batch Teaching with Sample Complexity Bounded by VCD
Authors: Farnam Mansouri, Hans Simon, Adish Singla, Sandra Zilles
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper is the first to provide an upper bound of VCD on a batch teaching complexity parameter. This parameter, called STDmin, is introduced here as a model of teaching that intuitively incorporates a notion of importance of an example for a concept. In designing the STDmin teaching model, we argue that the standard notion of collusion-freeness from the literature may be inadequate for certain applications; we hence propose three desirable properties of teaching complexity and demonstrate that they are satisfied by STDmin. Our main results are that the corresponding complexity parameter is upper-bounded by both RTD and VCD. This makes our paper the first to provide an upper bound of VCD or O(VCD) on a complexity parameter for batch teaching. |
| Researcher Affiliation | Academia | Farnam Mansouri University of Toronto EMAIL Hans U. Simon Max Planck Institute for Informatics EMAIL Adish Singla Max Planck Institute for Software Systems EMAIL Sandra Zilles University of Regina EMAIL |
| Pseudocode | No | The paper describes algorithmic steps in prose within the text (e.g., 'Next, we define Tk for k > 1 by the following algorithm. 1. For j = k, let Tk(Cj) = Tk 1(Cj). 2. To define Tk(Ck), initialize Tk(Ck) := T(Ck).'), but it does not present them in a formally structured 'Pseudocode' or 'Algorithm' block or figure. |
| Open Source Code | No | The paper does not contain any statements about releasing source code for the described methodology, nor does it provide links to any code repositories. |
| Open Datasets | No | The paper uses illustrative examples such as 'The concept class Cpair u [Zilles et al., 2011], for the case u = 3' in Table 1, but it does not refer to or provide access information for any publicly available datasets used for training or evaluation in the context of empirical experiments. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper focuses on theoretical concepts and does not describe any computational experiments; therefore, it does not provide any hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe software implementations or dependencies with version numbers. |
| Experiment Setup | No | The paper is purely theoretical and does not include any experimental setup details such as hyperparameters or system-level training settings. |