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..
Fast DPP Sampling for Nystrom with Application to Kernel Methods
Authors: Chengtao Li, Stefanie Jegelka, Suvrit Sra
ICML 2016 | Venue PDF | 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 EMAIL Stefanie Jegelka EMAIL Suvrit Sra EMAIL 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. |