Fast DPP Sampling for Nystrom with Application to Kernel Methods
Authors: Chengtao Li, Stefanie Jegelka, Suvrit Sra
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present several empirical results that support our theoretical analysis, and demonstrate the superior performance of DPP-based landmark selection compared with existing approaches. |
| Researcher Affiliation | Academia | Chengtao Li CTLI@MIT.EDU Stefanie Jegelka STEFJE@MIT.EDU Suvrit Sra SUVRIT@MIT.EDU Massachusetts Institute of Technology |
| Pseudocode | Yes | Algorithm 1 Gibbs sampler for c-DPP |
| Open Source Code | No | No explicit statement providing concrete access to source code for the methodology described in this paper was found. |
| Open Datasets | Yes | We use 8 datasets: Abalone, Ailerons, Elevators, Comp Act, Comp Act(s), Bank32NH, Bank8FM and California Housing4. We subsample 4,000 points from each dataset (3,000 training and 1,000 test). 4http://www.dcc.fc.up.pt/ ltorgo/ Regression/Data Sets.html |
| Dataset Splits | Yes | We subsample 4,000 points from each dataset (3,000 training and 1,000 test)... Throughout our experiments, we use an RBF kernel and choose the bandwidth σ and regularization parameter λ for each dataset by 10fold cross-validation. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running experiments were found. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) were found. |
| Experiment Setup | Yes | Throughout our experiments, we use an RBF kernel and choose the bandwidth σ and regularization parameter λ for each dataset by 10fold cross-validation. We initialize the Gibbs sampler via Kmeans++ and run for 3,000 iterations. Results are averaged over 3 random subsets of data. |