reproducibilityindex.ai

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 [1].

Less is More: Nyström Computational Regularization

Authors: Alessandro Rudi, Raffaello Camoriano, Lorenzo Rosasco

NeurIPS 2015 | Venue PDF | LLM Run Details

Reproducibility Variable	Result	LLM Response
Research Type	Experimental	Extensive experimental analysis shows that the considered approach achieves state of the art performances on benchmark large scale datasets.
Researcher Affiliation	Academia	Universit a degli Studi di Genova DIBRIS, Via Dodecaneso 35, Genova, Italy Istituto Italiano di Tecnologia i Cub Facility, Via Morego 30, Genova, Italy Massachusetts Institute of Technology and Istituto Italiano di Tecnologia Laboratory for Computational and Statistical Learning, Cambridge, MA 02139, USA
Pseudocode	Yes	Algorithm 1: Incremental Nystr om KRLS.
Open Source Code	Yes	2The code for Algorithm 1 is available at lcsl.github.io/Nystrom Co Re.
Open Datasets	Yes	We consider the pumadyn32nh (n = 8192, d = 32), the breast cancer (n = 569, d = 30), and the cpu Small (n = 8192, d = 12) datasets4. 4www.cs.toronto.edu/ delve and archive.ics.uci.edu/ml/datasets
Dataset Splits	Yes	We randomly split the training part in a training set and a validation set (80% and 20% of the n training points, respectively) for parameter tuning via cross-validation.
Hardware Specification	Yes	The model selection times, measured on a server with 12 2.10GHz Intel Xeon E5-2620 v2 CPUs and 132 GB of RAM, are reported in Figure 2.
Software Dependencies	No	The paper mentions 'Cholesky rank-one update formulas' and 'linear algebra libraries' but does not provide specific software names with version numbers for dependencies.
Experiment Setup	Yes	We empirically study the properties of Algorithm 1, considering a Gaussian kernel of width σ. The λ values are logarithmically spaced, while the m values are linearly spaced. The ranges and kernel bandwidths, chosen according to preliminary tests on the data, are σ = 2.66, λ ∈ [10−7, 1], m ∈ [10, 1000] for pumadyn32nh, σ = 0.9, λ ∈ [10−12, 10−3], m ∈ [5, 300] for breast cancer, and σ = 0.1, λ ∈ [10−15, 10−12], m ∈ [100, 5000] for cpu Small.