Researchers have established the first convergence guarantees for the plain vector-form Adam optimizer when subjected to heavy-tailed stochastic noise. This work addresses a gap in understanding Adam's behavior in settings where stochastic gradients have bounded p-th central moments, a scenario common in modern deep learning. The findings indicate that while Adam can converge to stationary points under heavy-tailed noise, it exhibits suboptimal iteration complexity and p-dependent convergence, though this suboptimality can be mitigated by knowing and controlling the domain radius. AI
IMPACT Provides theoretical insights into the robustness and limitations of a widely used optimization algorithm in deep learning.
RANK_REASON Academic paper detailing theoretical analysis of an optimization algorithm. [lever_c_demoted from research: ic=1 ai=1.0]
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