Optimal Algorithms for Online Convex Optimization with Adversarial Constraints
Authors: Abhishek Sinha, Rahul Vaze
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 4, we evaluate the practical performance of our algorithm in the online credit card fraud detection problem with a highly imbalanced dataset. |
| Researcher Affiliation | Academia | School of Technology and Computer Science Tata Institute of Fundamental Research Mumbai 400005, India |
| Pseudocode | Yes | Algorithm 1 Online Policy for COCO |
| Open Source Code | Yes | The code has been publicly released [Sinha, 2024b]. |
| Open Datasets | Yes | We experiment with a publicly available credit card transaction dataset [Dal Pozzolo et al., 2014]. |
| Dataset Splits | No | The paper states it learns 'in an entirely online fashion starting from random initialization' and does not specify traditional train/validation/test splits. |
| Hardware Specification | Yes | The network is then trained using Algorithm 1 on a quad-core CPU with 8 GB RAM. |
| Software Dependencies | No | The paper mentions training a neural network but does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | We choose a simple network architecture with a single hidden layer containing H = 10 hidden nodes and sigmoid nonlinearities. Initially, all weights are independently sampled from a standard normal distribution. By varying the hyperparameter λ, we obtain the ROC curve shown in Figure 1. |