The article introduces a new approach, RQP-SGD, for privacy-preserving machine learning. The rise of IoT devices necessitates efficient and secure data processing. Traditional machine learning models can be impractical for…
Continue readingThis article presents a unified framework to address the challenging convergence analysis under non-convex conditions in stochastic gradient descent (SGD). It interprets the updated direction as the sum of the…
Continue readingThis research paper focuses on the convergence rates of loss and uncertainty-based active learning algorithms. The authors first provide conditions for a convergence rate guarantee and apply this to linear…
Continue readingThe paper introduces DPSUR, a new privacy-preserving machine learning framework that selectively updates and releases data during model training. This technique is designed to protect against privacy attacks such as…
Continue readingThe paper addresses the challenge of performance degradation in Differentially Private Stochastic Gradient Descent with gradient clipping (DPSGD-GC), a tool for training deep learning models. The authors propose an error-feedback…
Continue readingDespite the widespread use of weight decay in training deep networks, its role is not well understood. This study demonstrates how weight decay modifies the optimization dynamics in overparameterized deep…
Continue readingThe article investigates the joint evolution of training dynamics via Stochastic Gradient Descent (SGD) and the spectra of empirical Hessian and gradient matrices. The authors demonstrate that in two canonical…
Continue readingLooks like you're using an ad blocker. Please support us by disabling these ad blocker.
