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..
SEAD: Unsupervised Ensemble of Streaming Anomaly Detectors
Authors: Saumya Gaurang Shah, Abishek Sankararaman, Balakrishnan Murali Narayanaswamy, Vikramank Singh
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on 14 non-trivial public datasets and an internal dataset corroborate our claims. |
| Researcher Affiliation | Industry | 1Amazon Web Services, Santa Clara, CA, USA. Correspondence to: Saumya Gaurang Shah <EMAIL>. |
| Pseudocode | Yes | The complete pseudo code is in Algorithm 1. Algorithm 1 SEAD Algorithm Algorithm 2 SEAD ++: Optimizing runtime by sampling |
| Open Source Code | No | The paper mentions using "open source implementations from Py SAD (Yilmaz & Kozat, 2020)" for base models but does not state that the code for SEAD itself is open-source, nor does it provide a link. |
| Open Datasets | Yes | We perform experiments on 15 datasets, of which 11 are from the Outlier Detection Data Sets (ODDS) (Rayana, 2016), 3 are from the USP Data Stream Repository (Souza et al., 2020) and one is an internal telemetry dataset from a multiserver database cloud service. |
| Dataset Splits | Yes | We set the first 100 data points for warm starting the base models and SEAD , but not for evaluation, i.e., is cold start . To overcome this issue, we split each dataset into chunks of 50 contiguous data points. |
| Hardware Specification | Yes | We performed all experiments on a single c5.2xlarge AWS EC2 instance. |
| Software Dependencies | No | The paper mentions using "open source implementations from Py SAD (Yilmaz & Kozat, 2020)" and "tdigest (Dunning & Ertl, 2019)" but does not provide specific version numbers for these or other software libraries/dependencies. |
| Experiment Setup | Yes | For our method SEAD , we choose hyperparameters η = 1, λ = 10 6 and π = Uniform distribution across all experiments. Table 11. Hyperparameter configurations for the base models. We set the first 100 data points for warm starting the base models and SEAD |