Online Adaptive Anomaly Thresholding with Confidence Sequences
Authors: Sophia Huiwen Sun, Abishek Sankararaman, Balakrishnan Murali Narayanaswamy
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our theoretical results with empirical evidence that our method outperforms commonly used baselines across synthetic and real world datasets. |
| Researcher Affiliation | Collaboration | 1University of California, San Diego. Work done when interning with Amazon Web Services. 2Amazon Web Services, Santa Clara CA. |
| Pseudocode | Yes | Algorithm 1 Decision making with confidence set; Algorithm 3 Thresholding without offline data; Algorithm 5 General thresholding algorithm |
| Open Source Code | No | The code to generate the synthetic dataset as well as implementations of our algorithms will be open sourced. |
| Open Datasets | Yes | We tested our algorithms on the MNIST dataset for one-class anomaly detection to demonstrate their real-world efficacy. |
| Dataset Splits | No | The paper mentions 'first 1500 as holdout' for Forest Cover and 'first 300 as holdout' for Mammography, but does not provide general or comprehensive train/validation/test dataset splits with specific percentages or counts for all datasets used, nor for the main MNIST experiments or real-world datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers needed for replication. |
| Experiment Setup | Yes | The metrics are abstention percentages (Abs. %) and mistake counts (FP + FN), averaged over 1000 streams each with 2000 samples drawn from Normal distributions with random parameters, with p = 1 10 2 and α = 10 3. |