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..
FALKON: An Optimal Large Scale Kernel Method
Authors: Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An extensive experimental analysis on large scale datasets shows that, even with a single machine, FALKON outperforms previous state of the art solutions, which exploit parallel/distributed architectures. |
| Researcher Affiliation | Academia | Alessandro Rudi INRIA Sierra Project-team, Ecole Normale Sup erieure, Paris Luigi Carratino University of Genoa Genova, Italy Lorenzo Rosasco University of Genoa, LCSL, IIT & MIT |
| Pseudocode | Yes | Algorithm 1 MATLAB code for FALKON. It requires O(n Mt + M 3) in time and O(M 2) in memory. See Sect. A and Alg. 2 in the appendixes for the complete algorithm. |
| Open Source Code | Yes | The code necessary to reproduce the following experiments, plus a FALKON version that is able to use the GPU, is available on Git Hub at https://github.com/LCSL/FALKON_paper . |
| Open Datasets | Yes | Million Songs [36] (Table 2, n = 4.6 105, d = 90, regression). [36] Thierry Bertin-Mahieux, Daniel P. W. Ellis, Brian Whitman, and Paul Lamere. The million song dataset. In ISMIR, 2011. ... IMAGENET (Table 3, n = 1.3 106, d = 1536, multiclass classification). We report the top 1 c-err over the validation set of ILSVRC 2012 with a single crop. |
| Dataset Splits | Yes | For datasets which do not have a fixed test set, we set apart 20% of the data for testing. ... We used a Gaussian kernel with diagonal matrix width learned with cross validation on a small validation set, λ = 10 8 and 105 Nystr om centers. |
| Hardware Specification | Yes | Indeed we used a single machine equipped with two Intel Xeon E5-2630 v3, one NVIDIA Tesla K40c and 128 GB of RAM and a basic MATLAB FALKON implementation |
| Software Dependencies | No | The paper mentions using a 'basic MATLAB FALKON implementation' but does not specify a version number for MATLAB or any other software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | Million Songs: We used a Gaussian kernel with σ = 6, λ = 10 6 and 104 Nystr om centers. ... TIMIT: We used the same preprocessed dataset of [6] and Gaussian Kernel with σ = 15, λ = 10 9 and 105 Nystr om centers. ... YELP: We used a linear kernel with 5 104 Nystr om centers. |