Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Fast DPP Sampling for Nystrom with Application to Kernel Methods
Authors: Chengtao Li, Stefanie Jegelka, Suvrit Sra
ICML 2016 | Venue PDF | LLM Run Details | Input Tokens: 17,650 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,327 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |