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..
Linear Submodular Bandits with a Knapsack Constraint
Authors: Baosheng Yu, Meng Fang, Dacheng Tao
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also conduct a number of experiments and the experimental results conο¬rm our theoretical analyses. |
| Researcher Affiliation | Academia | Centre for Quantum Computation and Intelligent Systems, University of Technology, Sydney Department of Computing and Information Systems, The University of Melbourne |
| Pseudocode | Yes | Algorithm 1 MCSGreedy Algorithm, Algorithm 2 CGreedy Algorithm |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | following the previous work (Yue and Guestrin 2011), we empirically evaluate our algorithms on simulation dataset by using news article recommendation (Li et al. 2010) as a case study. |
| Dataset Splits | No | The paper uses a "simulation dataset" but does not explicitly provide specific dataset split information (percentages, sample counts, or predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | In this simulation experiment, we randomly generate a d-dimensional vector (x1, x2, . . . , xd) to represent a news article a, where xi (0, 1)(for all i = 1, 2, . . . , d) represents the information coverage of a news article a on the topic i. For each news article a, we assume that it only has a limited number of main topics (xi > 0.5) and noisy topics (xi 0.5). The number of main topics is Nmain = 2 and the number of noise topics is Nnoise = 1. The topics coverage of each article are sampled from a uniform distribution. We randomly generate the w [0, 1]d, which is unknown to our algorithms, to represent a user s interest level in each topic. We also assume that a user will like some topics very much (w (i) 0.8) and will dislike other topics (w (i) 0.1). The costs set C = {c1, c2, . . . , cm} are sampled from the uniform distribution and normal distribution. |