3 accepted papers
Dynamic data pruning techniques aim to reduce computational cost while minimizing information loss, by periodically selecting representative subsets of input data during model training. However, existing methods often struggle to maintain strong worst-group accuracy, particularly at high pruning rat
Machine Learning as a Service (MLaaS) has simplified access to powerful machine learning models but faces challenges in complying with the “right to be forgotten” while resisting adversarial threats. Machine Unlearning (MU) addresses these issues by enabling selective data removal from models. Howev
Deep neural networks have achieved state-of-the-art performance in multiple domains but are increasingly resource-intensive, limiting their deployment on constrained devices. We introduce Structured Pruning via Ranking (SPvR), a novel structured pruning approach to address this challenge for classif