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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Maximizing Induced Cardinality Under a Determinantal Point Process
Authors: Jennifer A. Gillenwater, Alex Kulesza, Sergei Vassilvitskii, Zelda E. Mariet
NeurIPS 2018 | Venue PDF | 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. |