Data Independent

Data-independent methods in machine learning aim to develop algorithms and models that perform effectively without relying on specific data distributions or requiring extensive training on large datasets. Current research focuses on developing data-independent operators for tasks like deepfake detection and filter pruning in neural networks, as well as exploring the use of techniques like sketching and gradient boosting to achieve this goal in regression and clustering. These approaches offer advantages in terms of computational efficiency, privacy preservation, and generalizability across diverse datasets, impacting fields ranging from computer vision to federated learning.