Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …

A key element in solving real-life data science problems is selecting the types of models to
use. Tree ensemble models (such as XGBoost) are usually recommended for classification …

Academic tabular benchmarks often contain small sets of curated features. In contrast, data
scientists typically collect as many features as possible into their datasets, and even …

Self-supervised representation learning methods have achieved significant success in
computer vision and natural language processing (NLP), where data samples exhibit explicit …

Representation learning stands as one of the critical machine learning techniques across
various domains. Through the acquisition of high-quality features, pre-trained embeddings …

Motivation Piwi-interacting RNAs (piRNAs) play a critical role in the progression of various
diseases. Accurately identifying the associations between piRNAs and diseases is important …

Ensemble is a general way of improving the accuracy and stability of learning models,
especially for the generalization ability on small datasets. Compared with tree-based …

Despite groundbreaking success in image and text learning, deep learning has not
achieved significant improvements against traditional machine learning (ML) on tabular …

Recent work on deep learning for tabular data demonstrates the strong performance of deep
tabular models, often bridging the gap between gradient boosted decision trees and neural …

We explore unique considerations involved in fitting machine learning (ML) models to data
with very high precision, as is often required for science applications. We empirically …