SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation

Reject inference, augmentation, and sample selection

Many researchers see the need for reject inference in credit scoring models to come from a sample selection problem whereby a missing variable results in omitted variable bias. Alternatively, practitioners often see the problem as one of missing data where the relationship in the new model is biased because the behaviour of the omitted cases differs from that of those who make up the sample for...

Inefficiency of Data Augmentation for Large Sample Imbalanced Data

Many modern applications collect large sample size and highly imbalanced categorical data, with some categories being relatively rare. Bayesian hierarchical models are well motivated in such settings in providing an approach to borrow information to combat data sparsity, while quantifying uncertainty in estimation. However, a fundamental problem is scaling up posterior computation to massive sa...

Feature Selection for Small Sample Sets with High Dimensional Data Using Heuristic Hybrid Approach

Feature selection can significantly be decisive when analyzing high dimensional data, especially with a small number of samples. Feature extraction methods do not have decent performance in these conditions. With small sample sets and high dimensional data, exploring a large search space and learning from insufficient samples becomes extremely hard. As a result, neural networks and clustering a...

Rates of Convergence for Data Augmentation on Finite Sample Spaces

Deterministic Sample Sort For GPUs

We demonstrate that parallel deterministic sample sort for many-core GPUs (GPU Bucket Sort) is not only considerably faster than the best comparison-based sorting algorithm for GPUs (Thrust Merge [Satish et.al., Proc. IPDPS 2009]) but also as fast as randomized sample sort for GPUs (GPU Sample Sort [Leischner et.al., Proc. IPDPS 2010]). However, deterministic sample sort has the advantage that ...