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..
Prior-Free Dynamic Auctions with Low Regret Buyers
Authors: Yuan Deng, Jon Schneider, Balasubramanian Sivan
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that even in this prior-free setting, it is possible to extract a (1 ε)approximation of the full economic surplus for any ε > 0. The number of options offered to a buyer in any round scales independently of the number of rounds T and polynomially in ε. We show that this is optimal up to a polynomial factor; any mechanism achieving this approximation factor, even when values are drawn stochastically, requires at least Ω(1/ε) options. |
| Researcher Affiliation | Collaboration | Yuan Deng Duke University EMAIL Jon Schneider Google Research EMAIL Balasubramanian Sivan Google Research EMAIL |
| Pseudocode | Yes | Table 1: Construction of the i-th option |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | The paper is theoretical and does not use or refer to publicly available or open datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe dataset splits (training, validation, test) for empirical evaluation. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments requiring specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments requiring specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe a specific experimental setup with concrete hyperparameter values or training configurations. |