Parallel Streaming Wasserstein Barycenters
Authors: Matthew Staib, Sebastian Claici, Justin M. Solomon, Stefanie Jegelka
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate the practical effectiveness of our method, both in tracking moving distributions on a sphere, as well as in a large-scale Bayesian inference task. and We demonstrate the applicability of our method on two experiments, one synthetic and one performing a real inference task. |
| Researcher Affiliation | Academia | Matthew Staib MIT CSAIL mstaib@mit.edu, Sebastian Claici MIT CSAIL sclaici@mit.edu, Justin Solomon MIT CSAIL jsolomon@mit.edu, Stefanie Jegelka MIT CSAIL stefje@mit.edu |
| Pseudocode | Yes | Algorithm 1 Subgradient Ascent, Algorithm 2 Master Thread, Algorithm 3 Worker Thread |
| Open Source Code | Yes | We implemented our algorithm in C++ using MPI, and our code is posted at github.com/mstaib/stochastic-barycenter-code. |
| Open Datasets | Yes | We run logistic regression on the UCI skin segmentation dataset [8]. The 245057 datapoints are colors represented in R3, each with a binary label determing whether that color is a skin color. and [8] Rajen Bhatt and Abhinav Dhall. Skin segmentation dataset. UCI Machine Learning Repository. |
| Dataset Splits | No | The paper mentions splitting the dataset into 127 subsets for distributed processing, but it does not provide specific percentages or counts for training, validation, or test splits. It states 'Full experiment details are given in Appendix D.', but Appendix D is not provided in the current context. |
| Hardware Specification | No | The paper mentions using 'an InfiniBand cluster' and 'no individual 16 thread node used more than 2GB of memory', but it does not provide specific hardware details such as GPU/CPU models or comprehensive processor specifications. |
| Software Dependencies | No | The paper states 'We implemented our algorithm in C++ using MPI' and 'compute the barycenter LP as in [47] via Mosek [4]', but it does not specify version numbers for MPI, Mosek, or C++ compilers. |
| Experiment Setup | Yes | After 317 seconds, or about 10000 iterations per subset posterior, our algorithm has produced a barycenter on n 10^4 support points... and though tuning the stepsize becomes more challenging. and For n 10^4, over a wide range of stepsizes we can in seconds approximate the full posterior better than is possible with the LP as seen in Figure 2 by terminating early. |