Maximizing Induced Cardinality Under a Determinantal Point Process
Authors: Jennifer A. Gillenwater, Alex Kulesza, Sergei Vassilvitskii, Zelda E. Mariet
NeurIPS 2018 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments, We ran experiments with three types of kernel matrices:, Figure 2a shows the runtimes for GIC and SIC., In Figure 2c we plot the ratio of the GREEDY solution, GIC, to the optimum, MIC (for small n where it is possible to compute MIC by brute force)., Figure 2d shows the performance of the methods on each of the three types of kernels. |
| Researcher Affiliation | Collaboration | Jennifer Gillenwater Google Research NYC EMAIL Alex Kulesza Google Research NYC EMAIL Zelda Mariet Massachusetts Institute of Technology EMAIL Sergei Vassilvitskii Google Research NYC EMAIL |
| Pseudocode | No | The paper describes algorithms but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | The paper describes generating synthetic data (Wishart matrix, Cluster matrix, Graph Laplacian) for its experiments but does not use or provide access information for any pre-existing public datasets. |
| Dataset Splits | No | The paper describes generating synthetic data for experiments and does not specify any training/validation/test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | No | The paper describes parameters for generating the kernel matrices (e.g., 'cluster kernel uses 50 clusters', 'Laplacian kernel uses p = 0.2') but does not specify model training hyperparameters or system-level training settings. |