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..
Towards closing the gap between the theory and practice of SVRG
Authors: Othmane Sebbouh, Nidham Gazagnadou, Samy Jelassi, Francis Bach, Robert Gower
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We performed a series of experiments on data sets from LIBSVM [5] and the UCI repository [3], to validate our theoretical findings. We tested l2 regularized logistic regression on ijcnn1 and real-sim, and ridge regression on slice and Year Prediction MSD. We used two choices for the regularizer: λ = 10 1 and λ = 10 3. All of our code is implemented in Julia 1.0. Due to lack of space, most figures have been relegated to Section G in the supplementary material. |
| Researcher Affiliation | Academia | Othmane Sebbouh LTCI, T el ecom Paris Institut Polytechnique de Paris EMAIL Nidham Gazagnadou LTCI, T el ecom Paris Institut Polytechnique de Paris EMAIL Samy Jelassi ORFE Department Princeton University EMAIL Francis Bach INRIA Ecole Normale Sup erieure PSL Research University EMAIL Robert M. Gower LTCI, T el ecom Paris Institut Polytechnique de Paris EMAIL |
| Pseudocode | Yes | Algorithm 1 Free-SVRG Algorithm 2 L-SVRG-D |
| Open Source Code | No | The information is insufficient. The paper states 'All of our code is implemented in Julia 1.0.' but does not provide a link or explicit statement of open-source availability for the described methodology. |
| Open Datasets | Yes | We performed a series of experiments on data sets from LIBSVM [5] and the UCI repository [3] |
| Dataset Splits | No | The information is insufficient. The paper does not explicitly provide details about training, validation, or test dataset splits. It mentions 'training problems' and 'training sets' in a general context but not specific data partitioning for experiments. |
| Hardware Specification | No | The information is insufficient. The paper does not provide any specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The information is insufficient. The paper only states 'All of our code is implemented in Julia 1.0.' without specifying any versioned libraries or solvers. |
| Experiment Setup | Yes | We used two choices for the regularizer: λ = 10 1 and λ = 10 3. |