Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Improved learning rates in multi-unit uniform price auctions
Authors: Marius Potfer, Dorian Baudry, Hugo Richard, Vianney Perchet, Cheng Wan
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 27,244 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,751 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The answer NA means that the paper does not include experiments. If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not. |
| Researcher Affiliation | Collaboration | Marius Potfer 1,2 Dorian Baudry3 Hugo Richard1 Vianney Perchet1 1 Joint team Fairplay, ENSAE, and Criteo AI LAB 2 EDF R&D 3 Department of Statistics, University of Oxford |
| Pseudocode | Yes | Algorithm 1: Component based exponential weighting. Algorithm 2: Selection of the bids by a weight-pushing algorithm. |
| Open Source Code | No | The paper does not provide any statement or link indicating that its source code is open-source or publicly available. |
| Open Datasets | No | The paper is theoretical and does not involve experiments with datasets for training, validation, or testing. |
| Dataset Splits | No | The paper is theoretical and does not involve experiments with datasets, thus no training/test/validation splits are discussed. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments requiring hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not involve experiments, thus no experimental setup details like hyperparameters are provided. |