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..
Faster Greedy MAP Inference for Determinantal Point Processes
Authors: Insu Han, Prabhanjan Kambadur, Kyoungsoo Park, Jinwoo Shin
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, our algorithms are significantly faster than their competitors for large-scale instances, while sacrificing marginal accuracy. |
| Researcher Affiliation | Collaboration | 1School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. 2Bloomberg LP, 731 Lexington Avenue, New York, NY, 10069. |
| Pseudocode | Yes | Algorithm 1 Faster Greedy DPP Inference |
| Open Source Code | Yes | The codes are available in https://github.com/insuhan/fastdppmap. |
| Open Datasets | Yes | Details of the primaries are provided in http://www.presidency.ucsb.edu/debates.php. We use 39 videos from a Youtube dataset (De Avila et al., 2011), and the trained DPP kernels from (Gong et al., 2014). |
| Dataset Splits | No | The paper describes the datasets and experimental parameters but does not explicitly provide details about training, validation, or test dataset splits (e.g., specific percentages or sample counts). |
| Hardware Specification | Yes | The experiments are performed using a machine with a hexa-core Intel CPU (Core i7-5930K, 3.5 GHz) and 32 GB RAM. |
| Software Dependencies | No | The paper mentions using NLTK for preprocessing but does not provide specific version numbers for NLTK or any other key software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | Unless stated otherwise, we choose parameters of p = 5, k = 10, s = 50, m = 20 and n = 15, regardless matrix dimension, for our algorithms. We also run CG until it achieves convergence error less than 10 10 and typically TCG 30. |