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..
Dictionary Learning for Massive Matrix Factorization
Authors: Arthur Mensch, Julien Mairal, Bertrand Thirion, Gael Varoquaux
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the efficiency of our approach on massive functional Magnetic Resonance Imaging (f MRI) data, and on matrix completion problems for recommender systems, where we obtain significant speed-ups compared to state-of-the art coordinate descent methods. |
| Researcher Affiliation | Academia | Arthur Mensch EMAIL Parietal team, Inria, CEA, Paris-Saclay University. Neurospin, Gif-sur-Yvette, France Julien Mairal EMAIL Thoth team, Inria, Grenoble, France Bertrand Thirion EMAIL Gaël Varoquaux EMAIL Parietal team, Inria, CEA, Paris-Saclay University. Neurospin, Gif-sur-Yvette, France |
| Pseudocode | Yes | Procedure 1 Dictionary Learning for Massive Data |
| Open Source Code | Yes | We use scikit-learn (Pedregosa et al., 2011) in experiments, and have released a python package1 for reproducibility. 1http://github.com/arthurmensch/modl |
| Open Datasets | Yes | We validate the performance of the proposed algorithm on recommender systems for explicit feedback, a well-studied matrix completion problem. We evaluate the scalability of our method on datasets of different dimension: Movie Lens 1M, Movie Lens 10M, and 140M ratings Netflix dataset. |
| Dataset Splits | Yes | For Movielens datasets, we use a random 25% of data for test and the rest for training. We average results on five train/test split for Movie Lens in Table 1. On Netflix, the probe dataset is used for testing. Regularization parameter λ is set by cross-validation on the training set: the training data is split 3 times, keeping 33% of Movielens datasets for evaluation and 1% for Netflix, and grid search is performed on 15 values of λ between 10 2 and 10. |
| Hardware Specification | Yes | Benchmarking were run using a single 2.7 GHz Xeon CPU, with a 30 components dictionary. |
| Software Dependencies | No | The paper mentions 'scikit-learn (Pedregosa et al., 2011)' and 'python package' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | Regularization parameter λ is set by cross-validation on the training set: the training data is split 3 times, keeping 33% of Movielens datasets for evaluation and 1% for Netflix, and grid search is performed on 15 values of λ between 10 2 and 10. We use mini-batches of size n 100. |