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..
Low-Rank Regression with Tensor Responses
Authors: Guillaume Rabusseau, Hachem Kadri
NeurIPS 2016 | Venue PDF | 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. |