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..
A loss framework for calibrated anomaly detection
Authors:
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | While our focus has been mostly conceptual, some illustrative experiments are presented in Appendix D. |
| Researcher Affiliation | Academia | Aditya Krishna Menon Australian National University EMAIL Robert C. Williamson Australian National University EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper is primarily theoretical and does not specify any particular datasets used for training or their public availability. |
| Dataset Splits | No | The paper is theoretical and does not provide specific dataset split information for validation. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values or training configurations. |