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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions
Authors: Wei Zhang, Yanjun Han, Zhengyuan Zhou, Aaron Flores, Tsachy Weissman
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we complement the theoretical results with demonstrations using real bidding data. [...] This section presents several real-data experiments in repeated first-price auctions based on practical bidding data |
| Researcher Affiliation | Collaboration | 1MIT EECS 2MIT IDSS 3Arena Technologies 4NYU Stern 5Yahoo! Research 6Stanford EE EMAIL EMAIL EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1: Multiplicative Weights with Hint Intervals [...] Algorithm 2 (pseudocode in Appendix A) |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. The checklist indicates "Pseudocode is included but data is confidential", implying no code release. |
| Open Datasets | No | This section presents several real-data experiments in repeated first-price auctions based on practical bidding data, where the hint is the context-based prediction provided by blackbox machine learning models. For business confidentiality we do not disclose further information about the datasets. |
| Dataset Splits | No | The paper mentions using "around 0.38 million data points" and allocating "all data points to separate bins" but does not specify train, validation, or test dataset splits (e.g., percentages or counts). |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or cloud computing instances used for running the experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | We implemented Algorithm 3 on dataset with support size K = 5. [...] We allocate all data points to separate bins according to the private value vt |