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..
OKRidge: Scalable Optimal k-Sparse Ridge Regression
Authors: Jiachang Liu, Sam Rosen, Chudi Zhong, Cynthia Rudin
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments We test the effectiveness of our OKRidge on synthetic benchmarks and sparse identification of nonlinear dynamical systems (SINDy)[19]. |
| Researcher Affiliation | Academia | Jiachang Liu, Sam Rosen, Chudi Zhong, Cynthia Rudin Duke University EMAIL, EMAIL |
| Pseudocode | Yes | In Appendix E, we provide visual illustrations of Bn B and beam search as well as the complete pseudocodes of our algorithms. |
| Open Source Code | Yes | Implementations of OKRidge discussed in this paper are available at https://github.com/jiachangliu/OKRidge. |
| Open Datasets | No | The paper uses synthetic data generated according to a described process, and refers to dynamical systems (Lorenz, Hopf, MHD) via the Py SINDy library [27, 42] and a model paper [24], but does not provide direct access links or formal citations for the specific datasets used in their experiments. |
| Dataset Splits | Yes | Model selection was performed via cross-validation with training data encompassing the first 2/3rds of a trajectory and the final third used for validation. |
| Hardware Specification | Yes | All experiments were run on the 10x Tensor EX TS2-673917-DPN Intel Xeon Gold 6226 Processor, 2.7Ghz. We set the memory limit to be 100GB. |
| Software Dependencies | Yes | The Gurobi version is 10.0, which can be installed through conda (https://anaconda.org/gurobi/ gurobi). We used the Academic Site License. We implemented the perspective formulations and relaxed convex optimal perspective formulations in MOSEK. The MOSEK version is 10.0, which can be installed through conda (https://anaconda.org/MOSEK/mosek). |
| Experiment Setup | Yes | We set a 1-hour time limit and an optimality gap of relative tolerance 10 4. We use a value of 0.001 for λ2. For both MIOSR and our method OKRidge, the ridge regression hyperparameter choices are λ2 {10 5, 10 3, 10 2, 0.05, 0.2}, and the sparsity level hyperparameter choices are k {1, 2, 3, 4, 5}. As in the experiments of MIOSR [9], we set the time limit for each optimization to be 30 seconds for both MIOSR and our method OKRidge. |