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..
Understanding the Effect of Bias in Deep Anomaly Detection
Authors: Ziyu Ye, Yuxin Chen, Haitao Zheng
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We establish the first finite sample rates for estimating the relative scoring bias for deep anomaly detection, and empirically validate our theoretical results on both synthetic and real-world datasets. We also provide an extensive empirical study on how a biased training anomaly set affects the anomaly score function and therefore the detection performance on different anomaly classes. |
| Researcher Affiliation | Academia | Ziyu Ye , Yuxin Chen and Haitao Zheng University of Chicago EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Computing the anomaly detection threshold for Problem 3.2 |
| Open Source Code | No | The paper does not provide a link to its source code or explicitly state that the code is open-source. |
| Open Datasets | Yes | We empirically validate our assumptions and theoretical results on both synthetic and three real-world datasets4 (Fashion-MNIST, Stat Log (Landsat Satellite), and Cellular Spectrum Misuse [Li et al., 2019]). |
| Dataset Splits | Yes | To set the threshold, we fix the target FPR to be 0.05, and vary the number of normal data in the validation set n from {100, 1K, 10K}. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | Detailed descriptions on datasets and training configurations are listed in Appendix C. (Appendix C is not provided in the paper text). |