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..
Weakly Supervised Anomaly Detection via Dual-Tailed Kernel
Authors: Walid Durani, Tobias Nitzl, Claudia Plant, Christian Böhm
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, WSAD-DT achieves state-of-the-art performance on several challenging anomaly detection benchmarks, outperforming leading ensemble-based methods such as XGBOD. ... 8. Experiments 8.1. Experimental Setup We compare WSAD-DT with state-of-the-art deep anomaly detection methods on over 20 real-world datasets from the Ad Benchmark repository (Han et al., 2022). Each dataset is split into 70% training and 30% testing, preserving the anomaly ratio via stratified sampling. |
| Researcher Affiliation | Academia | 1LMU Munich, Munich Center for Machine Learning (MCML), Munich, Germany 2LMU Munich, Munich, Germany 3Faculty of Computer Science, ds:Uni Vie, University of Vienna, Vienna, Austria 4Faculty of Computer Science, University of Vienna, Vienna, Austria. |
| Pseudocode | Yes | G. Algorithm details In Algo. 1 we describe WSAD-DT in detail. |
| Open Source Code | No | Our code is implemented in Py Torch and builds on top of the Deep OD and Py OD libraries (Zhao et al., 2019; Xu, 2023). Our anonymous code repository: Link (Anonymous). |
| Open Datasets | Yes | We compare WSAD-DT with state-of-the-art deep anomaly detection methods on over 20 real-world datasets from the Ad Benchmark repository (Han et al., 2022). |
| Dataset Splits | Yes | Each dataset is split into 70% training and 30% testing, preserving the anomaly ratio via stratified sampling. |
| Hardware Specification | Yes | All experiments were conducted on a workstation equipped with an Intel Core i7-10700K CPU (3.8 GHz) and 32 GB of RAM. |
| Software Dependencies | No | Our code is implemented in Py Torch and builds on top of the Deep OD and Py OD libraries (Zhao et al., 2019; Xu, 2023). All models are trained for 100 epochs using the Adam optimizer with a learning rate of 1e-3 and a weight decay of 1e-5. We use the standard Adam hyperparameters (β1 = 0.9, β2 = 0.999). |
| Experiment Setup | Yes | All models are trained for 100 epochs using the Adam optimizer with a learning rate of 1e-3 and a weight decay of 1e-5. We use the standard Adam hyperparameters (β1 = 0.9, β2 = 0.999). Batches of size 64 are used for each training step (Table 7). ... Table 7. Neuralnetwork and training setting of WSAD-DT: GENERAL TRAINING Batch size 64 Learning rate 1e-3 Epochs 100 OPTIMIZER Optimizer Adam Momentum β1 0.9 Momentum β2 0.999 Weight decay 1e-5 |