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..
Data-Driven Knowledge-Aware Inference of Private Information in Continuous Double Auctions
Authors: Lvye Cui, Haoran Yu
AAAI 2024 | Venue PDF | 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 EMAIL, EMAIL |
| 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. |