Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization
Authors: Umut Simsekli, Cagatay Yildiz, Than Huy Nguyen, Taylan Cemgil, Gael Richard
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform several experiments on both synthetic and real datasets. The results support our theory and show that the proposed algorithm provides a significant speedup over the recently proposed synchronous distributed L-BFGS algorithm. |
| Researcher Affiliation | Academia | 1LTCI, T el ecom Paris Tech, Universit e Paris-Saclay, 75013, Paris, France 2Department of Computer Science, Aalto University, Espoo, 02150, Finland 3Department of Computer Engineering, Bo gazic i University, 34342, Bebek, Istanbul, Turkey. |
| Pseudocode | Yes | Algorithm 1: as-L-BFGS: Master node; Algorithm 2: as-L-BFGS: Worker node (w) |
| Open Source Code | No | The paper mentions 'This code can be used both in a distributed environment or a single computer with multiprocessors' but does not provide an explicit statement of public release or a link to the source code for the described methodology. |
| Open Datasets | Yes | Movie Lens 1Million (ML-1M), 10Million (ML-10M), and 20Million (ML20M) (grouplens.org) |
| Dataset Splits | No | The paper does not explicitly provide specific training/test/validation dataset splits, percentages, or sample counts. |
| Hardware Specification | No | We have conducted these experiments on a cluster of more than 500 interconnected computers, each of which is equipped with variable quality CPUs and memories. |
| Software Dependencies | No | For real data experiments, we have implemented all the three algorithms in C++ by using a low-level message passing protocol for parallelism, namely the Open MPI library. This does not specify version numbers for C++ or Open MPI. |
| Experiment Setup | Yes | In these experiments, we set K = 5 for all the three datasets and we set the number of workers to W = 10. |