Stable Optimization

Stable optimization focuses on developing algorithms that reliably and efficiently converge to optimal solutions, addressing challenges like noisy gradients, data distribution shifts, and the inherent instability of some model architectures, particularly in deep learning. Current research emphasizes techniques such as modified gradient descent methods (e.g., clipped SGD, $\mu^2$-SGD), novel loss functions (e.g., multi-scale perceptual loss), and strategies to mitigate inter-block optimization entanglement in knowledge distillation. These advancements are crucial for improving the robustness and performance of machine learning models across diverse applications, including federated learning, privacy-preserving prediction, and medical image reconstruction.