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..
Projective Quadratic Regression for Online Learning
Authors: Wenye Ma5093-5100
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the performance of the proposed PQR model in terms of accuracy and efficiency by comparing with the state-of-the-art methods. |
| Researcher Affiliation | Industry | Wenye Ma Tencent EMAIL |
| Pseudocode | Yes | Algorithm 1: Follow-The-Regularized-Leader |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code for the described methodology or a direct link to a code repository. |
| Open Datasets | Yes | For the online rating prediction task, we use the typical Movie Lens datasets, including Movie Lens-100K (ML100K), Movie Lens-1M (ML1M) and Movie Lens-10M (ML10M); For the online binary classification task, we select high-dimensional sparse datasets including Avazu, Criteo3, and KDD20124. These three high-dimensional datasets are preprocessed and can be downloaded from the LIBSVM website5. |
| Dataset Splits | Yes | All these datasets are randomly separated into train(80%), validation(10%) and test(10%) sets. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., CPU, GPU models, or cloud computing instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'FTRL-Proximal algorithm' and 'online gradient descent' but does not specify version numbers for any software, libraries, or frameworks used in the implementation. |
| Experiment Setup | Yes | The rank of FM is selected from the set of {2, 4, 8, 16, 32, 64, 128, 256, 512}. The coefficient of regularization term and the learning rate are tuned in a range [0.0001, 10]. |