Implicit Bias of Per-sample Adam on Separable Data: Departure from the Full-batch Regime

The article titled "Implicit Bias of Per-sample Adam on Separable Data: Departure from the Full-batch Regime" examines the behavior of the Adam optimizer when applied incrementally to logistic regression tasks involving linearly separable data. Adam, a widely used optimization algorithm in deep learning, has well-established practical applications, yet its theoretical underpinnings remain incompletely understood, particularly outside the full-batch training scenario. This study focuses on the implicit bias introduced by per-sample updates of Adam, contrasting it with the more commonly analyzed full-batch regime. By investigating this specific context, the research contributes to a deeper understanding of how Adam operates in incremental settings, which are prevalent in many machine learning workflows. The findings highlight a departure from the behavior expected under full-batch assumptions, suggesting that the optimizer's dynamics can vary significantly depending on the data processing approach. This work aligns with ongoing efforts in the AI community to clarify the theoretical foundations of optimization methods used in deep learning. Overall, the article provides valuable insights into the nuanced performance of Adam beyond traditional batch training frameworks.