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..
Statistical analysis of stochastic gradient methods for generalized linear models
Authors: Panagiotis Toulis, Edoardo Airoldi, Jason Rennie
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our set of experiments confirm our theory and more broadly suggest that the implicit procedure can be a competitive choice for fitting large-scale models, especially when robustness is a concern. We illustrate the different aspects of our theory on three separate sets of experiments. |
| Researcher Affiliation | Collaboration | Department of Statistics, Harvard University, 1 Oxford Street, Cambridge, MA 02138, USA. Google, Inc., 1600 Amphitheatre Pkwy, Mountain View, CA 94043 |
| Pseudocode | Yes | Algorithm 1 Implicit learning of canonical GLMs |
| Open Source Code | Yes | The full version of the paper together with the accompanying source code and documentation can be found at the following location: http://www.people.fas.harvard.edu/ptoulis/harvard-homepage/implicit-sgd.html. |
| Open Datasets | Yes | We implement an implicit online learning procedure for a SVM model and compare it to a standard SGD method on the RCV1 benchmark. |
| Dataset Splits | No | The paper mentions 'Test errors' on the RCV1 dataset but does not specify the train/validation/test splits, their percentages, or sample counts, or refer to standard splits with citations. |
| Hardware Specification | No | The paper does not specify any particular hardware components such as GPU or CPU models, memory, or specific computing platforms used for the experiments. |
| Software Dependencies | No | The paper mentions using 'Bottou’s SVM SGD implementation' and 'Our implicit SVM' but does not specify versions for any software, libraries, or frameworks used in the experiments. |
| Experiment Setup | No | The paper mentions learning rate schedules (e.g., an = α/n) and regularization parameters (λ), but it does not provide a comprehensive set of hyperparameters, optimizer settings, or other detailed training configurations necessary for reproducibility. |