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..
Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization
Authors: Jonathan Lacotte, Mert Pilanci
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Further, we show numerically that it outperforms standard iterative solvers such as the conjugate gradient method and its pre-conditioned version on several standard machine learning datasets. Numerical simulations were carried out on a 512Gb desktop station and implemented in Python using its standard numerical linear algebra modules3. |
| Researcher Affiliation | Academia | Jonathan Lacotte Department of Electrical Engineering Stanford University EMAIL Mert Pilanci Department of Electrical Engineering Stanford University EMAIL |
| Pseudocode | Yes | Algorithm 1: Adaptive Polyak-IHS method. |
| Open Source Code | Yes | Code is publicly available at https://github.com/jonathanlctt/eff_dim_solver |
| Open Datasets | Yes | We present in Figures 1 and 2 results for two standard datasets (see Appendix A.1 for additional experiments): (i) one-vs-all classification of MNIST digits and (ii) one-vs-all classification of CIFAR10 images. |
| Dataset Splits | No | The paper mentions using standard datasets like MNIST and CIFAR10 but does not specify train/validation/test splits or a validation set. |
| Hardware Specification | No | The paper states "Numerical simulations were carried out on a 512Gb desktop station", but does not specify CPU or GPU models, or other detailed hardware specifications. |
| Software Dependencies | No | The paper states "implemented in Python using its standard numerical linear algebra modules", but does not provide specific version numbers for Python or any libraries used. |
| Experiment Setup | Yes | Algorithm 1 provides inputs such as "initial sketch size m 1, initial points x0, x1 Rd, target convergence rates cgd, cp (0, 1), gradient descent step size µgd, Polyak step size µp 0 and momentum parameter βp 0". Also, in section 5, it mentions "For each value of ν, we stop the algorithm once ε = 10^-10-precision is reached." |