Automated Mechanism Design without Money via Machine Learning
Authors: Harikrishna Narasimhan, Shivani Agarwal, David C. Parkes
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real-world data confirm the usefulness of our methods. |
| Researcher Affiliation | Academia | Harikrishna Narasimhan Harvard University hnarasimhan@seas.harvard.edu Shivani Agarwal Radcliffe Institute for Advanced Study, Harvard University Indian Institute of Science shivani@csa.iisc.ernet.in David C. Parkes Harvard University parkes@seas.harvard.edu |
| Pseudocode | No | No pseudocode or algorithm blocks are provided in the paper. |
| Open Source Code | No | The paper does not provide concrete access to source code for the described methodology. |
| Open Datasets | No | The paper mentions generating synthetic data and using data 'obtained from the Wake County, NC Public School System' but does not provide concrete access information (link, DOI, formal citation with author/year) for any dataset used. |
| Dataset Splits | Yes | We generate 1000 examples, divided into equal train-validation-test sets, with the validation set used for parameter tuning in structural SVM. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper mentions general optimization methods like 'standard gradient-based method (with multiple random restarts)' and 'parameter tuning in structural SVM' but does not provide specific hyperparameter values or detailed training configurations. |