Sample Efficient Decentralized Stochastic Frank-Wolfe Methods for Continuous DR-Submodular Maximization
Authors: Hongchang Gao, Hanzi Xu, Slobodan Vucetic
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results confirm the effectiveness of the proposed methods.In addition to the theoretical considerations, we perform extensive experimental evaluation to confirm the effectiveness of our proposed methods. |
| Researcher Affiliation | Academia | Hongchang Gao , Hanzi Xu and Slobodan Vucetic Department of Computer and Information Sciences, Temple University, PA, USA {hongchang.gao, tun47067, vucetic}@temple.edu |
| Pseudocode | Yes | Algorithm 1 De SVRFW-gp |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Here, we use two datasets: Movie Lens-1M and Movie Lens-100K 3. 3https://grouplens.org/datasets/movielens/ |
| Dataset Splits | No | The paper mentions distributing data across workers ('For each case, the ratings from users are divided into all workers evenly.') but does not provide specific train/validation/test dataset splits, percentages, or methodology for partitioning the data for model training and evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | In our experiment, we use 8 workers for Movie Lens-1M and 4 workers for Movie Lens-100K. For each case, the ratings from users are divided into all workers evenly. As for the communication graph, we use the Erdos-Renyi random graph in our experiment. The mean vertex degree in the graph is 2. For the non-diagonal entries in the weight matrix W of the communication graph, if vertex i and vertex j are connected, wij = 1/(1 + max(Di, Dj)) where Di denotes the degree of the vertex i. If there is no edge between vertex i and vertex j, wij equals to 0. For the diagonal entries, wii = 1 P j N (i) wij. ... for our methods, we use the batch size of 100 for Movie Lens-100K and 200 for Movie Lens-1M. |