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].
Online Task Assignment with Controllable Processing Time
Authors: Ruoyu Wu, Wei Bao, Liming Ge
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate OMLA against five benchmarks...We generate the synthetic data set in the experiment...In Figure 1, we investigate the ratio between the online performance of OMLA and LP(Off)...In Figure 2, we compare the performance of OMLA with benchmarks. |
| Researcher Affiliation | Academia | Ruoyu Wu , Wei Bao , Liming Ge School of Computer Science, The University of Sydney EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 OMLA Algorithm; Algorithm 2 Calculation of Activation and Baseline Values |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code for the described methodology or a link to a code repository. |
| Open Datasets | No | We generate the synthetic data set in the experiment. (The approach was also adopted in [Sumita et al., 2022].) |
| Dataset Splits | No | The paper describes generating synthetic data for experiments and running multiple rounds of experiments with randomly generated task sequences, but it does not specify explicit training, validation, and testing dataset splits with percentages or counts. |
| Hardware Specification | No | The paper describes the experimental setup but does not provide any specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers, such as programming languages, libraries, or solvers used for implementation or experimentation. |
| Experiment Setup | Yes | We set |U| = 10, |V | = 25, and T = 100. For each e E, we set qe U(0.5, 1) and ru,v,l U(a l0.2, a l0.4), where a U(0.5, 1). For each l L we set the distribution Dl as a binomial distribution B(T, l1.2/20). For settings (a), (b) in Figure 2 and (a) (b) in Figure 3, u is drawn uniformly from [ ]. We set the rejection penalty for level l as Ξl = l + 2. |