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..
Randomized Block-Diagonal Preconditioning for Parallel Learning
Authors: Celestine Mendler-Dünner, Aurelien Lucchi
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our main contribution is to demonstrate that the convergence of these methods can significantly be improved by a randomization technique which corresponds to repartitioning coordinates across tasks during the optimization procedure. We provide a theoretical analysis that accurately characterizes the expected convergence gains of repartitioning and validate our findings empirically on various traditional machine learning tasks. |
| Researcher Affiliation | Academia | 1University of California, Berkeley 2ETH Zürich. |
| Pseudocode | Yes | Algorithm 1 Block-Diagonal Preconditioning for (1) with (i) static and (ii) dynamic partitioning |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We have chosen the gisette, the mushroom and the covtype dataset that can be downloaded from (Dua & Graff, 2017) |
| Dataset Splits | No | The paper does not specify explicit training/validation/test dataset splits (e.g., percentages or counts). It mentions 'Validation of Convergence Rates' but this refers to validating theoretical results, not a dataset split. |
| Hardware Specification | No | The paper mentions 'multi-core machine with shared memory' but does not specify any exact GPU/CPU models, processor types, or memory details used for the experiments. |
| Software Dependencies | No | The paper mentions using existing algorithms like COCOA, ADN, and LS but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, specific library versions). |
| Experiment Setup | Yes | If not stated otherwise we use λ = 1. We perform this experiment for different values of K and α. ...with a fixed step size η. |