Data-Driven Knowledge-Aware Inference of Private Information in Continuous Double Auctions
Authors: Lvye Cui, Haoran Yu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on a large experimental dataset, and demonstrate the superior performance of our framework over baselines in inferring the private information of humans. |
| Researcher Affiliation | Academia | Lvye Cui, Haoran Yu School of Computer Science & Technology, Beijing Institute of Technology cui lvye@outlook.com, yhrhawk@gmail.com |
| Pseudocode | Yes | Algorithm 1: Behavior Learning Algorithm |
| Open Source Code | Yes | Our data and codes are available at: https://github.com/cuilvye/Inference-CDAs. |
| Open Datasets | Yes | We conduct all experiments on a large experimental CDA dataset (Lin et al. 2020). |
| Dataset Splits | Yes | We randomly select 80% of all market-seller pairs for training, 10% for validation, and 10% for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' and 'GRU network' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For the hyperparameters w1, w2, and w3, we use the grid search method to optimize them in a discretized space, minimizing the MSE on the validation data. Their ultimate values are 0.2, 0.6, and 0.2, respectively, and we apply the Adam optimizer with a learning rate of 0.001 for network training. |