Less is More: Nyström Computational Regularization
Authors: Alessandro Rudi, Raffaello Camoriano, Lorenzo Rosasco
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | 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. |