Improved learning rates in multi-unit uniform price auctions
Authors: Marius Potfer, Dorian Baudry, Hugo Richard, Vianney Perchet, Cheng Wan
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |