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..
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. |