Linear Learner

Linear learners, simple yet powerful models that map input features to outputs via a linear transformation, remain a significant area of research due to their interpretability and efficiency. Current work focuses on enhancing their robustness to adversarial attacks, improving their performance in online learning settings (e.g., for real-time control), and developing methods for incorporating them into larger architectures like federated learning and neural networks to improve interpretability and mitigate biases. These advancements are crucial for applications ranging from real-time robotic control and data privacy to improving the reliability and trustworthiness of machine learning models in various domains.