Projection Robust Wasserstein Barycenters

Authors: Minhui Huang, Shiqian Ma, Lifeng Lai

ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We incorporate the RPRWB into a discrete distribution clustering algorithm, and the numerical results on real text datasets confirm that our RPRWB model helps improve the clustering performance significantly.
Researcher Affiliation Academia 1 Department of Electrical and Computer Engineering, University of California, Davis, CA, USA 2 Department of Mathematics, University of California, Davis, CA, USA.
Pseudocode Yes Algorithm 1 The RGA-IBP Algorithm; Algorithm 2 The RBCD Algorithm; Algorithm 3 Round(π, p, q)
Open Source Code Yes Code available at https://github.com/mhhuang95/PRWB.
Open Datasets Yes We use the pre-trained word-vector dataset Glo Ve (Pennington et al., 2014)... The Reuters Subset is a 5-class subset of the Reuters dataset 2. The BBCnews Abstract and BBCsport Abstract 3 (Greene & Cunningham, 2006)...
Dataset Splits No The paper describes using datasets for clustering performance evaluation and mentions AMI scores, but does not explicitly detail a validation dataset split or usage.
Hardware Specification Yes All experiments are conducted on a Linux server with a 32-core Intel Xeon CPU (E5-2667, v4, 3.20GHz per core).
Software Dependencies No The paper mentions using Glo Ve 300d word vectors but does not specify software dependencies with version numbers for the implementation of the proposed algorithms or other experimental tools.
Experiment Setup Yes We set the step size τ = 0.0005 for both RBCD and RGA-IBP algorithms and η = 0.5 mid({Cl}l [m])... We set ϵ = 10 4... We choose k = 2 for the BBCsport Abstract dataset and k = 3 for the Reuters Subset and the BBCnews Abstract datasets.