36 citations · 212 across the 30 of their papers we have counts for
5 papers · 1 filter
Interpretable Latent Variables in Deep State Space Models
Haoxuan Wu, David S. Matteson, Martin T. Wells
We introduce a new version of deep state-space models (DSSMs) that combines a recurrent neural network with a state-space framework to forecast time series data. The model estimate…
Robust Matrix Completion with Mixed Data Types
Daqian Sun, Martin T. Wells
We consider the matrix completion problem of recovering a structured low rank matrix with partially observed entries with mixed data types. Vast majority of the solutions have prop…
A Double Parametric Bootstrap Test for Topic Models
Skyler Seto, Sarah Tan, Giles Hooker +1
Non-negative matrix factorization (NMF) is a technique for finding latent representations of data. The method has been applied to corpora to construct topic models. However, NMF ha…
Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable
Sarah Tan, Matvey Soloviev, Giles Hooker +1
Ensembles of decision trees perform well on many problems, but are not interpretable. In contrast to existing approaches in interpretability that focus on explaining relationships…
Supervised Classification Using Sparse Fisher's LDA
Irina Gaynanova, James G. Booth, Martin T. Wells
It is well known that in a supervised classification setting when the number of features is smaller than the number of observations, Fisher's linear discriminant rule is asymptotic…