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 [1].
Randomized Learning-Augmented Auctions with Revenue Guarantees
Authors: Ioannis Caragiannis, Georgios Kalantzis
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We characterize all pairs of parameters γ and ρ so that a randomized γ-consistent and ρ-robust auction exists. Furthermore, for the setting in which robustness can be a function of the prediction error, we give sufficient and necessary conditions for the existence of robust auctions and present randomized auctions that extract a revenue that is only a polylogarithmic (in terms of the prediction error) factor away from the highest agent valuation. The proof follows in Sections 4.1 and 4.2. |
| Researcher Affiliation | Academia | Ioannis Caragiannis and Georgios Kalantzis Department of Computer Science, Aarhus University EMAIL, EMAIL |
| Pseudocode | No | The paper provides mathematical definitions and formulas for allocation and payment functions (e.g., in Section 3.1 'Proving the If Part of Theorem 3' and Section 4.1 'Proving the If Part of Theorem 4'), but these are not presented as structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and focuses on mathematical proofs and conditions for existence of auctions. It does not mention any source code for its methodology or provide links to a repository. |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies or datasets, therefore no information about dataset availability or access is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with data, thus there is no mention of training/validation/test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not involve software implementations or dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not conduct experiments, therefore no experimental setup details such as hyperparameters or training settings are provided. |