A Trust-region Framework for Moment Estimation
Quick summary
arXiv:2608.04026v1 Announce Type: cross Abstract: In this paper, we develop a trust-region framework for understanding the behavior of adaptive moment estimation mechanisms, such as \textsc{Adam}, in stochastic gradient optimization. Specifically, in this framework, the magnitude of the update step for each individual weight is constrained within a trust-region governed by a moment constraint of order $p\in[2,4]$. The resulting derivation then leads to a family of learning-rate mechanisms based on second-moment estimation and a normalized $p$-th moment estimation. When $p=4$, this involves kur
Key takeaways
- arXiv:2608.04026v1 Announce Type: cross Abstract: In this paper, we develop a trust-region framework for understanding the behavior of adaptive moment estimation mechanisms, such as \textsc{Adam}, in stochastic gradient optimization.
- Specifically, in this framework, the magnitude of the update step for each individual weight is constrained within a trust-region governed by a moment constraint of order $p\in[2,4]$.
- The resulting derivation then leads to a family of learning-rate mechanisms based on second-moment estimation and a normalized $p$-th moment estimation.
Why it matters
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