Faster Greedy MAP Inference for Determinantal Point Processes
Authors: Insu Han, Prabhanjan Kambadur, Kyoungsoo Park, Jinwoo Shin
ICML 2017 | Conference PDF | Archive PDF | Plain Text | 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. |