On Fast Leverage Score Sampling and Optimal Learning
Authors: Alessandro Rudi, Daniele Calandriello, Luigi Carratino, Lorenzo Rosasco
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments Leverage scores accuracy. We first study the accuracy of the leverage scores generated by BLESS and BLESS-R, comparing SQUEAK [8] and Recursive-RLS (RRLS) [9]. We begin by uniformly sampling a subsets of n = 7 104 points from the SUSY dataset [19], and computing the exact leverage scores (i, λ) using a Gaussian Kernel with σ = 4 and λ = 10 5, which is at the limit of our computational feasibility. We then run each algorithm to compute the approximate leverage scores e JH(i, λ), and we measure the accuracy of each method using the ratio e JH(i, λ)/ (i, λ) (R-ACC). The final results are presented in Figure 1. |
| Researcher Affiliation | Academia | Alessandro Rudi INRIA Sierra team, Daniele Calandriello LCSL IIT & MIT, Genoa, Italy Luigi Carratino University of Genoa, Genoa, Italy Lorenzo Rosasco University of Genoa, LCSL IIT & MIT |
| Pseudocode | Yes | Algorithm 1 Bottom-up Leverage Scores Sampling (BLESS) and Algorithm 2 Bottom-up Leverage Scores Sampling without Replacement (BLESS-R) |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We begin by uniformly sampling a subsets of n = 7 104 points from the SUSY dataset [19] |
| Dataset Splits | No | The paper uses the SUSY and HIGGS datasets but does not explicitly provide specific training/validation/test dataset split information such as percentages, sample counts, or a detailed splitting methodology. |
| Hardware Specification | Yes | We gratefully acknowledge the support of NVIDIA Corporation for the donation of the Titan Xp GPUs and the Tesla k40 GPU used for this research. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers (e.g., 'PyTorch 1.9', 'Python 3.8'). |
| Experiment Setup | Yes | For the SUSY dataset we use a Gaussian Kernel with σ = 4, λfalkon = 10 6, λbless = 10 4 obtaining MH ' 104 Nyström centres. For the HIGGS dataset we use a Gaussian Kernel with σ = 22, λfalkon = 10 8, λbless = 10 6, obtaining MH ' 3 104 Nyström centres. |