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Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics
May 14, 2024, 4:42 a.m. | Haoyang Zheng, Hengrong Du, Qi Feng, Wei Deng, Guang Lin
cs.LG updates on arXiv.org arxiv.org
Abstract: Replica exchange stochastic gradient Langevin dynamics (reSGLD) is an effective sampler for non-convex learning in large-scale datasets. However, the simulation may encounter stagnation issues when the high-temperature chain delves too deeply into the distribution tails. To tackle this issue, we propose reflected reSGLD (r2SGLD): an algorithm tailored for constrained non-convex exploration by utilizing reflection steps within a bounded domain. Theoretically, we observe that reducing the diameter of the domain enhances mixing rates, exhibiting a \emph{quadratic} …
abstract arxiv cs.ai cs.lg datasets distribution dynamics exploration gradient however issue replica scale simulation stat.ml stochastic the simulation type via
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