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].
Linear Combinatorial Semi-Bandit with Causally Related Rewards
Authors: Behzad Nourani-Koliji, Saeed Ghoorchian, Setareh Maghsudi
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments using synthetic and real-world datasets demonstrate the superior performance of our proposed method compared to several benchmarks. |
| Researcher Affiliation | Academia | Behzad Nourani-Koliji1 , Saeed Ghoorchian2 and Setareh Maghsudi1 1University of T ubingen 2Technical University of Berlin EMAIL EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 SEM-UCB: Structural Equation Model-Upper Confidence Bound |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the open-sourcing of the code for the described methodology. |
| Open Datasets | Yes | We focus on the recorded daily new cases from 10 August to 15 October, 2020, for N = 21 regions within Italy.1 The Covid-19 dataset only provides us with the overall daily new cases of each region. ... 1https://github.com/pcm-dpc/COVID-19 |
| Dataset Splits | Yes | We split the data into train and validation sets in 10:1 ratio. More specifically, we split the data into 6 subsets of 11 consecutive days. In each subset, one day is chosen uniformly at random to be included in the validation set while the remaining 10 days are added to the train set. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU/GPU models, memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'OSQP solver [Stellato et al., 2020]' as an example for computational complexity but does not provide specific version numbers for this or any other software dependencies used in the experiments. |
| Experiment Setup | Yes | Finally, in our experiments, we choose s = 6, meaning that the algorithms can choose 6 base arms at each time of play. ... The regularization parameter λ is tuned by grid search over [0.0001, 1000]. |