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 [1].
Randomized Truthful Auctions with Learning Agents
Authors: Gagan Aggarwal, Anupam Gupta, Andres Perlroth, Grigoris Velegkas
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The answer NA means that the paper does not include experiments. |
| Researcher Affiliation | Collaboration | Gagan Aggarwal Google Research EMAIL Anupam Gupta New York University, Google Research EMAIL Andres Perlroth Google Research EMAIL Grigoris Velegkas Yale University EMAIL |
| Pseudocode | Yes | ALGORITHM 1: Multiplicative Weights Update Algorithm. |
| Open Source Code | No | The answer NA means that paper does not include experiments requiring code. |
| Open Datasets | No | The paper does not include experiments or use datasets for training. |
| Dataset Splits | No | The paper does not include experiments or specify dataset splits for validation. |
| Hardware Specification | No | The paper does not include experiments and therefore does not provide hardware specifications. |
| Software Dependencies | No | The paper does not include experiments and therefore does not provide specific software dependencies with version numbers for replication. |
| Experiment Setup | No | The paper does not include experiments and therefore does not provide specific experimental setup details like hyperparameters. |