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Adam Optimizer Convergence Revisited in New Research Paper

By PulseAugur Editorial · [2 sources] ·

This paper revisits the convergence properties of the Adam optimization algorithm, demonstrating that projected Adam with arbitrary moment decay parameters can exhibit regret bounded away from zero. The authors extend this finding to several Adam variants, including AdamW, RMSProp, and NAdam, and also analyze an i.i.d. variant of the Adam algorithm. The work builds upon previous research by Reddi, Kale, and Kumar, relaxing their constraints on the moment decay parameters. AI

IMPACT Provides theoretical insights into the convergence of optimization algorithms crucial for training large AI models.

RANK_REASON The item is an academic paper published on arXiv discussing theoretical aspects of an optimization algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Adam Optimizer Convergence Revisited in New Research Paper

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Steven Heilman, Sampad Mohanty ·

    On the Convergence of Adam, Revisited

    arXiv:2607.03519v1 Announce Type: cross Abstract: We show that projected Adam for online optimization with arbitrary moment decay parameters $\beta_1,\beta_2\in[0,1)$ can have average regret bounded away from zero. A similar result of Reddi-Kale-Kumar from 2018 required $\beta_1<…

  2. arXiv stat.ML TIER_1 English(EN) · Sampad Mohanty ·

    On the Convergence of Adam, Revisited

    We show that projected Adam for online optimization with arbitrary moment decay parameters $β_1,β_2\in[0,1)$ can have average regret bounded away from zero. A similar result of Reddi-Kale-Kumar from 2018 required $β_1<\sqrt{β_2}$. Similar to their result, we use a three-periodic …

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