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 [1].
NysADMM: faster composite convex optimization via low-rank approximation
Authors: Shipu Zhao, Zachary Frangella, Madeleine Udell
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments on real-world datasets demonstrate that Nys ADMM can solve important applications, such as the lasso, logistic regression, and support vector machines, in half the time (or less) required by standard solvers. |
| Researcher Affiliation | Academia | 1Cornell University, Ithaca, NY, USA. 2Stanford University, Stanford, CA, USA. |
| Pseudocode | Yes | Algorithm 1 ADMM input feature matrix A, response b, loss function â, regularization g and h, stepsize Ď repeat... Algorithm 4 Randomized Nystr om Approximation... Algorithm 7 Ada Nys ADMM |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the described methodology or provide a link to a code repository. |
| Open Datasets | Yes | These experiments use datasets with n > 10, 000 or d > 10, 000 from LIBSVM (Chang & Lin, 2011), UCI (Dua & Graff, 2017), and Open ML (Vanschoren et al., 2013), with statistics summarized in Table 2. |
| Dataset Splits | No | The paper mentions using datasets for experiments and refers to 'E2006.train' in a dataset name, but it does not specify explicit train/validation/test dataset splits, percentages, or sample counts for reproducibility. |
| Hardware Specification | Yes | We run all experiments on a server with 128 Intel Xeon E74850 v4 2.10GHz CPU cores and 1056GB. |
| Software Dependencies | No | The paper mentions using 'SAGA algorithm...implemented in sklearn' and 'LIBSVM solver (Chang & Lin, 2011)', but it does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | The tolerance of Nys ADMM at each iteration is chosen as the geometric mean Îľk+1 = q rkprk d of the ADMM primal residual rp and dual residual rd at the previous iteration... We choose a sketch size s = 50 to compute the Nystr om approximation throughout our experiments. |