Distributed estimation of the inverse Hessian by determinantal averaging
Authors: Michal Derezinski, Michael W. Mahoney
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 1: Newton step estimation error versus number of machines, averaged over 100 runs (shading is standard error) for a libsvm dataset [CL11]. More plots in Appendix C. |
| Researcher Affiliation | Academia | Michał Derezi nski Department of Statistics University of California, Berkeley mderezin@berkeley.edu Michael W. Mahoney ICSI and Department of Statistics University of California, Berkeley mmahoney@stat.berkeley.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | Figure 1: Newton step estimation error versus number of machines, averaged over 100 runs (shading is standard error) for a libsvm dataset [CL11]. More plots in Appendix C. |
| Dataset Splits | No | The paper mentions using a 'libsvm dataset' but does not provide specific training/validation/test split information (exact percentages, sample counts, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |