Stable Algorithm

Stable algorithms in machine learning aim to develop training methods that are robust to noise, adversarial attacks, and numerical instability, ensuring reliable and accurate model performance. Current research focuses on improving the stability of stochastic gradient descent (SGD) variants through techniques like incorporating memory and employing Moreau envelope methods, particularly within the context of adversarial training and multi-agent reinforcement learning. These efforts are crucial for enhancing the reliability and trustworthiness of machine learning models across diverse applications, addressing issues like robust overfitting and improving the generalization capabilities of algorithms in complex environments. The development of comprehensive databases documenting numerical stability issues and solutions further contributes to building more robust and dependable machine learning systems.