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..
Towards Consistency in Adversarial Classification
Authors: Laurent Meunier, Raphael Ettedgui, Rafael Pinot, Yann Chevaleyre, Jamal Atif
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experi- mental results (either in the supplemental material or as a URL)? [N/A] (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A] (c) Did you report error bars (e.g., with respect to the random seed after running experi- ments multiple times)? [N/A] (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A] |
| Researcher Affiliation | Collaboration | Laurent Meunier1,2 Raphaël Ettedgui1 Rafael Pinot3 Yann Chevaleyre1 Jamal Atif1 1Université Paris-Dauphine, PSL Research University, CNRS, LAMSADE, Paris, France 2Meta AI Research, Paris, France 3 École Polytechnique Fédérale de Lausanne (EPFL), Switzerland |
| Pseudocode | No | The paper is theoretical and does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experi- mental results (either in the supplemental material or as a URL)? [N/A] |
| Open Datasets | No | 3. If you ran experiments... (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A] 4. If you are using existing assets (e.g., code, data, models) or curating/releasing new assets... (a) If your work uses existing assets, did you cite the creators? [N/A] |
| Dataset Splits | No | 3. If you ran experiments... (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A] |
| Hardware Specification | No | 3. If you ran experiments... (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A] |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | 3. If you ran experiments... (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A] |