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].
The Two-Sided Game of Googol
Authors: José Correa, Andrés Cristi, Boris Epstein, José Soto
JMLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | For the former objective we obtain a simple 0.45292-competitive algorithm. For the latter, we obtain a 0.63518-competitive algorithm. Our main contribution is to set up a model allowing to transform probabilistic optimal stopping problems into purely combinatorial ones. For instance, we can apply our results to obtain lower bounds for the single sample prophet secretary problem. Keywords: optimal stopping, prophet inequalities, data-driven decision making, secretary problem, sampling. |
| Researcher Affiliation | Academia | Jos e Correa EMAIL Department of Industrial Engineering Universidad de Chile Santiago, 8320000, Chile Andres Cristi EMAIL Department of Industrial Engineering Universidad de Chile Santiago, 8320000, Chile Boris Epstein EMAIL Decision, Risk and Operations Division Columbia Business School New York, NY 10027, USA Jos e Soto EMAIL Department of Mathematical Engineering and Center for Mathematical Modeling CNRS IRL 2807 Universidad de Chile Santiago, 8320000, Chile |
| Pseudocode | No | The paper describes algorithms such as the 'Open moving window algorithm (ALGo)', 'Closed moving window algorithm (ALGc)', and 'Full window algorithm (ALGf)' in descriptive text, outlining their strategies. However, it does not present these or any other procedures in structured pseudocode blocks or figures explicitly labeled as 'Algorithm' or 'Pseudocode'. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the described methodology, nor does it provide links to code repositories or mention code in supplementary materials. |
| Open Datasets | No | The paper describes and analyzes theoretical algorithms for a game (The Two-Sided Game of Googol) and its variants. It refers to 'instances' of the problem and uses conceptual scenarios and distributions (e.g., 'independent draws from a distribution Fi') rather than specific, named, or publicly accessible datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments using specific datasets. Therefore, there is no mention of dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper focuses on theoretical analysis, algorithm design, and mathematical proofs. It does not describe any experimental procedures that would require specific hardware, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper presents theoretical algorithms and competitive analysis. It does not include details on specific software components or libraries with version numbers that would be necessary for reproducing experimental results. |
| Experiment Setup | No | The paper is a theoretical work presenting algorithms, proofs, and competitive ratios. It does not describe any empirical experimental setup, hyperparameters, training configurations, or system-level settings for reproducing practical results. |