Bayesian Learning With Unbounded Capacity From Heterogeneous And Set-Valued Data
Abstract
Large-scale and modern datasets have reshaped machine learning research and practices. They are notonly bigger in size, but predominantly heterogeneous and growing in their complexity. This proposalaims to advance machine learning methods grounded in the theory of recent Bayesian nonparametrics todeal with growing complexity and heterogeneity of large-scale data. The proposal is unique in its approachto deliver three new bodies of theory and techniques for: a) Bayesian nonparametric methods thatcan express and inference from heterogeneous, set-valued data sources with infinite model capacity, b)new framework for deterministic fast inference based on small-variance asymptotic analysis (SVAA) andWasserstein geometry and c) new applications in pervasive healthcare and exploiting electronic medicalrecords (EMR) data.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 05, 2016
- Source ID
- FA23861614138
Entities
People
- Dinh Phung
Organizations
- Air Force Office of Scientific Research
- Deakin University
- United States Air Force