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..
Bandits with Concave Aggregated Reward
Authors: Yingqi Yu, Sijia Zhang, Shaoang Li, Lan Zhang, Wei Xie, Xiang-Yang Li
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive simulations demonstrate that our algorithms achieve better results than the most advanced bandit algorithms. |
| Researcher Affiliation | Academia | University of Science and Technology of China, Hefei, China Institute of Artificial Intelligence, Hefei Comprehensive National Science Center |
| Pseudocode | Yes | Algorithm 1: SW-BCAR |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes generating data from 'truncated normal distributions' for simulations but does not refer to a publicly available dataset with a specific name, link, or formal citation. |
| Dataset Splits | No | The paper describes simulation parameters and performance evaluation over different settings, but it does not specify training, validation, and test dataset splits in the context of data partitioning for reproduction. |
| Hardware Specification | No | The paper discusses simulations and evaluations but does not specify any hardware details like GPU/CPU models or memory used for running the experiments. |
| Software Dependencies | No | The paper refers to benchmark algorithms but does not provide specific software names with version numbers for implementation dependencies (e.g., Python, PyTorch). |
| Experiment Setup | Yes | In the experiments, variables other than specified separately were fixed as follows: 1) the round number T = 20000; the arm number K = 2; 2) the optimal arm s mean value ยต = 0.8; the suboptimal arms mean values ยต(a) = 0.4; 3) the aggregated reward function f(x) = 1 + x 1; 4) the parameter for the value range ฯ = 2. |