Balanced Representation Learning

Balanced representation learning aims to mitigate biases stemming from imbalanced datasets, crucial for accurate causal inference and prediction tasks. Current research focuses on developing methods that achieve balanced representations while avoiding over-balancing, often employing adversarial training, hierarchical attention mechanisms, or multi-task learning within novel model architectures. This work is significant for improving the reliability of causal effect estimations (e.g., treatment effects) in observational studies and enhancing the performance of machine learning models on imbalanced datasets across diverse applications, such as action recognition and personalized medicine.