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..
Higher-Order Factorization Machines
Authors: Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the proposed approaches on four different link prediction tasks. 6 Experimental results |
| Researcher Affiliation | Collaboration | Mathieu Blondel, Akinori Fujino, Naonori Ueda NTT Communication Science Laboratories Japan Masakazu Ishihata Hokkaido University Japan |
| Pseudocode | Yes | Algorithm 1 Evaluating Am(p, x) in O(dm) Algorithm 2 Computing Am(p, x) in O(dm) |
| Open Source Code | No | The paper does not provide an explicit statement or a link to the open-source code for the methodology described. |
| Open Datasets | Yes | Table 2: Datasets used in our experiments. For a detailed description, c.f. Appendix A. NIPS [17] Enzyme [21] GD [10] Movielens 100K [6] |
| Dataset Splits | Yes | We split the n+ positive samples into 50% for training and 50% for testing. We sample the same number of negative samples as positive samples for training and use the rest for testing. |
| Hardware Specification | No | The paper mentions running experiments on the NIPS dataset and comparing solvers but does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for these experiments. |
| Software Dependencies | No | The paper mentions `libfm` and `TensorFlow` but does not specify version numbers for any software dependencies used in their experiments or implementation. |
| Experiment Setup | Yes | We chose β from 10^-6, 10^-5, . . . , 10^6 by cross-validation and following [9] we empirically set k = 30. Throughout our experiments, we initialized the elements of P randomly by N(0, 0.01). We set ℓ to be the squared loss. |