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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Online Algorithms for Rent-Or-Buy with Expert Advice
Authors: Sreenivas Gollapudi, Debmalya Panigrahi
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test the efficacy of our algorithms via simulations. We set the buying cost b = 1. (The actual value of b is unimpor-tant because we can scale all values by b.) We choose the actual outcome x to be a value uniformly drawn from [0, 2b]. We vary the number of experts from 1 to 8 and set their associated predictions to x + ϵ where ϵ is drawn from a normal distribution of mean 0 and standard deviation σ. To verify consistency and robustness of our algorithms, we vary σ from 0 to 2. Finally, for the algorithm in Fig. 4, we consider values of 0.1, 0.5, and 0.9 for the meta parameter λ. We label the algorithm defined in Figure 1 consistent; it s extension to handle non-zero prediction errors (see Section 3) as robust; and the robust and consistent algorithm in Section 4 as hybrid. Figure 3 illustrates the relative performance of our algorithms. We make three observations. |
| Researcher Affiliation | Collaboration | 1Google Research 2Department of Computer Science, Duke University. |
| Pseudocode | Yes | Figure 1. The algorithm for k experts with zero error; Figure 2. The algorithm for k experts with non-zero error; Figure 4. The hybrid algorithm for k experts |
| Open Source Code | No | No explicit statement or link providing access to the source code for the described methodology. |
| Open Datasets | No | We test the efficacy of our algorithms via simulations. We set the buying cost b = 1. ... We choose the actual outcome x to be a value uniformly drawn from [0, 2b]. We vary the number of experts from 1 to 8 and set their associated predictions to x + ϵ where ϵ is drawn from a normal distribution of mean 0 and standard deviation σ. |
| Dataset Splits | No | The paper describes simulations where data is generated for each trial, but does not specify explicit train/validation/test splits as typically used with fixed datasets. |
| Hardware Specification | No | No specific hardware details (GPU/CPU models, memory, or specific computer specifications) are mentioned for running the experiments. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned. |
| Experiment Setup | Yes | We set the buying cost b = 1. We choose the actual outcome x to be a value uniformly drawn from [0, 2b]. We vary the number of experts from 1 to 8 and set their associated predictions to x + ϵ where ϵ is drawn from a normal distribution of mean 0 and standard deviation σ. To verify consistency and robustness of our algorithms, we vary σ from 0 to 2. Finally, for the algorithm in Fig. 4, we consider values of 0.1, 0.5, and 0.9 for the meta parameter λ. |