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..
Stochastic Online Linear Regression: the Forward Algorithm to Replace Ridge
Authors: Reda Ouhamma, Odalric-Ambrym Maillard, Vianney Perchet
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Last, we provide numerical experiments to illustrate our results and endorse our intuitions. |
| Researcher Affiliation | Collaboration | Reda Ouhamma Univ. Lille, CNRS, Inria, Centrale Lille, UMR 9189 CRISt AL, F-59000 EMAIL Odalric. Maillard Univ. Lille, CNRS, Inria, Centrale Lille, UMR 9189 CRISt AL, F-59000 Vianney. Perchet Criteo, ENSAE, ENS PARIS-SACLAY |
| Pseudocode | Yes | Algorithm 1: Online ridge regression Algorithm 2: The forward algorithm Algorithm 3: OFULf algorithm |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See Appendix G |
| Open Datasets | No | The paper uses internally generated synthetic data for experiments and does not provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes online simulation experiments with generated data, and thus does not provide traditional train/validation/test dataset splits. It does not mention any explicit validation split. |
| Hardware Specification | No | The paper mentions that the type of resources used can be found in Appendix G, but no specific hardware details (e.g., GPU/CPU models, memory) are provided in the main text of the paper. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with versions) in the main text. |
| Experiment Setup | Yes | In Figures 2a and 2b we observe the effect of regularization on the performance of ridge and forward regressions in a 5-dimensional regression setting, we vary λ {1/T, 1/ log(T), 1, 10}, sample a zero mean Gaussian noise with σ = 0.1 and draw features uniformly from the unit ball. [...] We consider a 100-dimensional linear bandit with 10 arms, the parameter vector is drawn from the unit ball, actions are such that xt 200. Noise ϵt L= N(0, 10 1), λ = 10 5, δ = 10 3. |