Householder Reflection

Householder reflections, orthogonal transformations represented by matrices, are finding increasing use in diverse machine learning applications. Current research focuses on leveraging their properties for efficient dictionary learning, model adaptation (particularly in large language models and image generation), and improving the accuracy of Lipschitz bounds for neural networks. This involves developing algorithms that utilize chains of Householder reflections to achieve orthogonality and low-rank approximations, leading to improved computational efficiency and performance compared to existing methods. The resulting advancements contribute to more efficient and interpretable machine learning models with improved theoretical guarantees.