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].
Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint
Authors: Georgios Amanatidis, Federico Fusco, Philip Lazos, Stefano Leonardi, Rebecca Reiffenhäuser
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluation of our algorithms showcases their improved performance on real and synthetic data. ... In Section 5 we showcase the fact that our theoretical results indeed translate into applied performance. We focus on two applications that fit within the framework of non-monotone submodular maximization subject to a knapsack constraint, namely video recommendation and influence-and-exploit marketing. We run experiments on real and synthetic data that indicate that SAMPLEGREEDY consistently performs better than FANTOM while being much faster. |
| Researcher Affiliation | Academia | Georgios Amanatidis University of Essex & University of Amsterdam EMAIL Federico Fusco Sapienza University of Rome EMAIL Philip Lazos Sapienza University of Rome EMAIL Stefano Leonardi Sapienza University of Rome EMAIL Rebecca Reiffenhäuser Sapienza University of Rome EMAIL |
| Pseudocode | Yes | The pseudo-code for the resulting algorithm, ADAPTIVEGREEDY, is given below. |
| Open Source Code | No | The paper does not provide any explicit links or statements about the availability of its source code for the described methods. |
| Open Datasets | Yes | We evaluate SAMPLEGREEDY on an instance based on the latest version of the Movie Lens dataset [31]... [31] F. M. Harper and J. A. Konstan. The Movie Lens datasets: History and context. ACM Trans. Interact. Intell. Syst., 5(4), Dec. 2015. ... We evaluate ADAPTIVEGREEDY on an instance based on the You Tube graph [50]... [50] J. Yang and J. Leskovec. Defining and evaluating network communities based on ground-truth. Knowledge and Information Systems, 42(1):181 213, 2015. |
| Dataset Splits | No | The paper mentions using Movie Lens and You Tube graph datasets but does not specify any training, validation, or test splits (e.g., percentages, absolute counts, or cross-validation setup). |
| Hardware Specification | No | The paper describes the performance and speed of algorithms but does not specify any hardware details such as GPU/CPU models, memory, or computing infrastructure used for the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks) used for the experiments. |
| Experiment Setup | Yes | A delicate point is tuning the probabilities of acceptance p for improved performance. ... we guess a good value for it by running our algorithms 5 times, each with a p chosen uniformly at randomly from [0.9, 1]. ... We use λ = 3, µ = 7 and set the parameters α, β so that the two terms are of comparable size. ... We assume that the valuation function of each buyer i is of the form fi(x) = ai x where ai is drawn independently from a Pareto Type II distribution with λ = 1, α = 2. |