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..
One-shot Distributed Ridge Regression in High Dimensions
Authors: Yue Sheng, Edgar Dobriban
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our results are supported by simulations and real data analysis. ... Section 4 contains experiments on real data. ... We confirm these results in detailed simulation studies and on an empirical data example, using the Million Song Dataset. |
| Researcher Affiliation | Academia | 1Wharton Statistics Department, University of Pennsylvania, Philadelphia, PA, USA 2Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, USA. |
| Pseudocode | Yes | Algorithm 1: Optimally weighted distributed ridge regression |
| Open Source Code | No | No statement or link providing concrete access to source code for the methodology was found. |
| Open Datasets | Yes | Million Song Year Prediction Dataset (MSD) (Bertin-Mahieux et al., 2011). ... We download the dataset from the UC Irvine Machine Learning Repository. |
| Dataset Splits | Yes | For each experiment, we randomly choose ntrain = 10,000 samples from the training set and ntest = 1,000 samples from the test set. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for experiments are mentioned. |
| Software Dependencies | No | No specific software versions (e.g., Python 3.8, PyTorch 1.9) are mentioned. |
| Experiment Setup | Yes | We choose the number of machines to be k = 1, 10, 20, 50, 100, 500, 1, 000, and we distribute the data evenly across the k machines. ... We repeat the experiment T = 100 times... ... The estimator using only a fraction 1/k of the data, which is just one of the local estimators. For this estimator, we choose the tuning parameter λ = kp/(ntrain ˆα2). |