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..
Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization
Authors: Hadrien Hendrikx, Lin Xiao, Sebastien Bubeck, Francis Bach, Laurent Massoulie
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world datasets illustrate the benefits of acceleration in the ill-conditioned regime. We compare in this section the performances of SPAG with those of DANE and its heavyball acceleration, HB-DANE (Yuan and Li, 2019), as well as accelerated gradient descent (AGD). |
| Researcher Affiliation | Collaboration | 1INRIA, DIENS, PSL Research University, Paris, France. 2Microsoft Research, Redmond, WA, USA. |
| Pseudocode | Yes | Algorithm 1 SPAG(LF/φ, σF/φ, x0) |
| Open Source Code | Yes | We also provide code for SPAG in supplementary material. |
| Open Datasets | Yes | We use two datasets from Lib SVM1, RCV1 (Lewis et al., 2004) and the preprocessed version of KDD2010 (algebra) (Yu et al., 2010). Accessible at https://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/binary.html |
| Dataset Splits | No | The paper describes how local datasets are constructed ('by shuffling the Lib SVM datasets, and then assigning a fixed portion to each worker') and how a 'preconditioning dataset' is created ('the server subsamples n points from its local dataset'), but it does not specify explicit training/validation/test dataset splits with percentages, sample counts, or references to predefined splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'a sparse implementation of SDCA (Shalev-Shwartz, 2016)' as the method used for local subproblems, but it does not list any specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions or other libraries). |
| Experiment Setup | Yes | We initialize all algorithms at the same point, which is the minimizer of the server s entire local loss (with 105 samples regardless of how many samples are used for preconditioning). we choose LF/φ = 1 and tune µ. we use SPAG with σ 1 F/φ = 1 + 2µ/λ and HB-DANE with β = (1 (1 + 2µ/λ) 1/2)2. keep doing passes over the preconditioning dataset until Vt(xt) 10 9 (checked at each epoch). |