We are excited to announce that our paper “Baxter permutation process” has been accepted to NeurIPS2020 as a spotlight presentation.
In this paper, a Bayesian nonparametric model for Baxter permutations (BPs), termed BP process (BPP) is proposed and applied to relational data analysis. The BPs are a well-studied class of permutations, and it has been demonstrated that there is one-to-one correspondence between BPs and several interesting objects including floorplan partitioning (FP), which constitutes a subset of rectangular partitioning (RP). Accordingly, the BPP can be used as a floorplan partitioning (FP) model.
We combine the BPP with a multi-dimensional extension of the stick-breaking process called the block-breaking process to fill the gap between FPs and RPs, and obtain a stochastic process on arbitrary rectangular partitionings. Compared with conventional Bayesian nonparametric models for arbitrary rectangular partitionings, the proposed model is simpler and has a high affinity with Bayesian inference.
You can find a pre-proceedings paper at NeurIPS2020 Pre-Proceedings
Baxter Permutation Process Part of Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020) Bibtek download is not availble in the pre-proceeding Authors Masahiro Nakano, Akisato Kimura, Takeshi Yamada, Naonori Ueda Abstract In this paper, a Bayesian nonparametric (BNP) model for Baxter permutations (BPs), termed BP process (BPP) is proposed and applied to relational data analysis.
and a MATLAB/Python implementations at GitHub.
This is a MATLAB code for Bayesian nonparametric relational data analysis based on Baxter Permutation Process ( NeurIPS, 2020). The key features are listed as follows: Clustering based on rectangular partitioning: For an input relational matrix, it can discover disjoint rectangle blocks and suitable permutations of rows and columns.