Practical $0.385$-Approximation for Submodular Maximization Subject to a Cardinality Constraint
Authors: Morad Tukan, Loay Mualem, Moran Feldman
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, we evaluate the empirical performance of our algorithm in experiments based on the machine learning applications of Movie Recommendation, Image Summarization, and Revenue Maximization. These experiments demonstrate the efficacy of our approach. |
| Researcher Affiliation | Collaboration | Murad Tukan Data Heroes Israel murad@dataheroes.ai Loay Mualem Department of Computer Science University of Haifa Haifa Israel loaymual@gmail.com Moran Feldman Department of Computer Science University of Haifa Haifa Israel moranfe@cs.haifa.ac.il |
| Pseudocode | Yes | Algorithm 1: FAST-LOCAL-SEARCH(k, f, ε, L) |
| Open Source Code | Yes | The implementations code can be found at https://github.com/muradtuk/ 385Approximation Sub Max. |
| Open Datasets | Yes | Movie Lens data set [18] which includes 10,437 movies. (i) CIFAR10 [21] A dataset of 50,000 images belonging to 10 different classes (categories). (ii) CIFAR100 [21] A dataset of 50,000 images belonging to 100 different classes. (iii) Tiny Image Net [24] A dataset of 100,000 images belonging to 200 different classes. |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test dataset splits. |
| Hardware Specification | Yes | The experiments were performed on a 2.2GHz i9-13980HX (24 cores total) machine with 64GB RAM. |
| Software Dependencies | Yes | Our algorithms were implemented in Python 3.11 using mainly Numpy [19], and Numba [23]. |
| Experiment Setup | Yes | Throughout the experiments, we have set ε = 0.1; and all the reported results are averaged across 8 executions. |