Balanced Representation

Balanced representation in machine learning aims to mitigate biases stemming from imbalanced datasets or treatment assignment, improving the fairness and accuracy of models. Current research focuses on developing techniques like representation balancing and cost-sensitive learning, often integrated into neural networks (including CNNs and Transformers) or evolutionary algorithms, to achieve more equitable representation of different classes or treatment groups. This work is crucial for enhancing the reliability of AI systems across diverse applications, from autonomous driving safety to causal inference in healthcare and economics, ensuring fairer and more accurate predictions.