Search Results for author:
Found 3 papers, 1 papers with code
Dendrogram of mixing measures: Hierarchical clustering and model selection for finite mixture models
The dendrogram's construction is derived from the theory of convergence of the mixing measures, and as a result, we can both consistently select the true number of mixing components and obtain the pointwise optimal convergence rate for parameter estimation from the tree, even when the model parameters are only weakly identifiable.
Explaining Groups of Points in Low-Dimensional Representations
A common workflow in data exploration is to learn a low-dimensional representation of the data, identify groups of points in that representation, and examine the differences between the groups to determine what they represent.
Communication-Efficient Distributed Dual Coordinate Ascent
Communication remains the most significant bottleneck in the performance of distributed optimization algorithms for large-scale machine learning.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
