3DP3: 3D Scene Perception via Probabilistic Programming
Nishad Gothoskar, Marco Cusumano-Towner, et al.
NeurIPS 2021
An automated feature selection pipeline was developed using several state-of-the-art feature selection techniques to select optimal features for Differentiating Patterns of Care (DPOC). The pipeline included three types of feature selection techniques; Filters, Wrappers and Embedded methods to select the top K features. Five different datasets with binary dependent variables were used and their different top K optimal features selected. The selected features were tested in the existing multi-dimensional subset scanning (MDSS) where the most anomalous subpopulations, most anomalous subsets, propensity scores, and effect of measures were recorded to test their performance. This performance was compared with four similar metrics gained after using all covariates in the dataset in the MDSS pipeline. We found out that despite the different feature selection techniques used, the data distribution is key to note when determining the technique to use.
Nishad Gothoskar, Marco Cusumano-Towner, et al.
NeurIPS 2021
Akifumi Wachi, Yunyue Wei, et al.
NeurIPS 2021
Tian Gao, Shankar Subramaniam, et al.
NeurIPS 2021
Rahul Nair, Pierpaolo Tommasi
NeurIPS 2021