MAP Clustering

The goal of this project is to develop computationally efficient algorithms for maximum a-posterior clustering with constraints.

Participants

Publications

  • Cluster Trellis: Data Structures & Algorithms for Exact Inference in Hierarchical Clustering, Proceedings of Machine Learning Research (AISTATS2021) (2021)
  • A Global Optimization Algorithm for Sparse Mixed Membership Matrix Factorization, Contemporary Biostatistics with Biopharmaceutical Applications (2019)
  • Compact Representation of Uncertainty in Clustering, Advances in Neural Information Processing Systems (2018)