Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams
Authors: Ashkan Norouzi-Fard, Jakub Tarnawski, Slobodan Mitrovic, Amir Zandieh, Aidasadat Mousavifar, Ola Svensson
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that SALSA significantly outperforms the state of the art in applications related to exemplar-based clustering, social graph analysis, and recommender systems. |
| Researcher Affiliation | Academia | 1Theory of Computation Laboratory, EPFL, Lausanne, Vaud, Switzerland. |
| Pseudocode | Yes | In what follows, we provide pseudo-codes of our three algorithms. For sake of brevity, we fix the values of constants and give the full analysis of the algorithms in the full version of this paper. Algorithm 1 DENSE |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the source code for the methodology described is publicly available. |
| Open Datasets | Yes | We consider three graphs for this problem from the SNAP data library (Leskovec & Krevl, 2014). Pokec social network ... Live Journal social network ... Orkut social network ... Spambase This dataset consists of 4, 601 emails... (Lichman, 2013). CIFAR-10 This dataset consists of 50, 000 color images... (Krizhevsky et al., 2014). We use the Movielens 1M dataset (Harper & Konstan, 2016) to build a recommender system for movies. |
| Dataset Splits | No | The paper uses various datasets (Pokec, Live Journal, Orkut, Spambase, CIFAR-10, Movielens 1M) for empirical evaluation but does not provide explicit details about dataset splits (e.g., train/validation/test percentages or counts). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | For each of the experiments we invoke our algorithms with the following values of the parameters: Algorithm 1 with C1 = 10, C2 = 0.2, β = 0.8; Algorithm 2 with ε = 1/6; Algorithm 3 with β = 0.1, ε = 0.05, δ = 0.025. |