Low-Rank Regression with Tensor Responses
Authors: Guillaume Rabusseau, Hachem Kadri
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real data show that HOLRR computes accurate solutions while being computationally very competitive. |
| Researcher Affiliation | Academia | Guillaume Rabusseau and Hachem Kadri Aix Marseille Univ, CNRS, LIF, Marseille, France {firstname.lastname}@lif.univ-mrs.fr |
| Pseudocode | Yes | Algorithm 1 HOLRR Algorithm 2 Kernelized HOLRR |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of its proposed method. |
| Open Datasets | Yes | Meteo-UK: The data set is collected from the meteorological office of the UK2. It contains monthly measurements of 5 variables in 16 stations across the UK from 1960 to 2000. http://www.metoffice.gov.uk/public/weather/climate-historic/ |
| Dataset Splits | Yes | Hyper-parameters for all algorithms are selected using 3-fold cross-validation on the training data. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., CPU, GPU models) used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | Hyper-parameters for all algorithms are selected using 3-fold cross-validation on the training data. For all the experiments, we use 90% of the available data for training and 10% for testing. |