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..
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization
Authors: Shusen Wang, Fred Roosta, Peng Xu, Michael W. Mahoney
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct large-scale experiments on a computer cluster and, empirically, demonstrate the superior performance of GIANT. |
| Researcher Affiliation | Academia | Shusen Wang Stevens Institute of Technology EMAIL Farbod Roosta-Khorasani University of Queensland EMAIL Peng Xu Stanford University EMAIL Michael W. Mahoney University of California at Berkeley EMAIL |
| Pseudocode | No | The paper includes a diagram (Figure 1) illustrating an iteration of GIANT, but it does not provide formal pseudocode or an algorithm block. |
| Open Source Code | Yes | The Apache Spark code is available at https://github.com/ wangshusen/Spark Giant.git. |
| Open Datasets | Yes | We use three binary classification datasets: MNIST8M (digit 4 versus 9 , thus n = 2M and d = 784), Covtype (n = 581K and d = 54), and Epsilon (n = 500K and d = 2K), which are available at the LIBSVM website. |
| Dataset Splits | No | The paper states 'We randomly hold 80% for training and the rest for test.' but does not explicitly mention a separate validation set or its split details. |
| Hardware Specification | Yes | We conduct large-scale experiments on the Cori Supercomputer maintained by NERSC, a Cray XC40 system with 1632 compute nodes, each of which has two 2.3GHz 16-core Haswell processors and 128GB of DRAM. We use up to 375 nodes (12,000 CPU cores). |
| Software Dependencies | No | We implement GIANT, Accelerated Gradient Descent (AGD) [23], Limited memory BFGS (LBFGS) [12], and Distributed Approximate NEwton (DANE) [36] in Scala and Apache Spark [44]. While software is named, specific version numbers are not provided for Scala or Apache Spark. |
| Experiment Setup | Yes | Our theory requires the local sample size s = n m to be larger than d. But in practice, GIANT converges even if s is smaller than d. In this set of experiments, we set m = 89, and thus s is about half of d. |