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..
Submodular Maximization in Clean Linear Time
Authors: Wenxin Li, Moran Feldman, Ehsan Kazemi, Amin Karbasi
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experimental Results We have studied the submodular maximization problem subject to different constraints. In this section, we compare our proposed algorithms with the state-of-the-art algorithms under the following constraints: (i) a carnality constraint (Section 6.1), (ii) a single knapsack constraint (Section 6.2), and (iii) combination of a p-system and d knapsack constraints (Appendix J.4). ... In our first experiment, we implement a movie recommender system by finding a diverse set of movies for a user. We adopt the approach of Lindgren et al. [54] to extract features for each movie by using ratings from the Movie Lens dataset [31]. ... From Figures 1c and 1d, we observe that Algorithm 2 significantly outperforms the other two algorithms with respect to both the utility and number of oracle calls metrics. |
| Researcher Affiliation | Collaboration | Wenxin Li The Ohio State University EMAIL Moran Feldman University of Haifa EMAIL Ehsan Kazemi Google EMAIL Amin Karbasi Yale University, Google Research EMAIL. |
| Pseudocode | Yes | Algorithm 1: FAST THRESHOLD GREEDY(ε, α)... Algorithm 2: FAST THRESHOLD GREEDY + POST-PROCESSING(ε)... Algorithm 3: Basic Algorithm(λ, ρ) |
| Open Source Code | No | The paper states "Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes]", but it does not provide a direct link to a code repository or an unambiguous statement about releasing the source code for the described methodology. |
| Open Datasets | Yes | In our first experiment, we implement a movie recommender system by finding a diverse set of movies for a user. We adopt the approach of Lindgren et al. [54] to extract features for each movie by using ratings from the Movie Lens dataset [31]. |
| Dataset Splits | No | The paper mentions using datasets for experiments (e.g., Movie Lens dataset) but does not provide specific details on how the data was split into training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | FASTTHRESHOLDGREEDY with ε {0.1, 0.2} performs as good as LAZYGREEDY... Execute Algorithm 1 with α = ε 1. |