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pro vyhledávání: '"En, Matthew Ng Cheng"'
In this paper we develop a Stochastic Gradient Langevin Dynamics (SGLD) algorithm tailored for solving a certain class of non-convex distributionally robust optimisation problems. By deriving non-asymptotic convergence bounds, we build an algorithm w
Externí odkaz:
http://arxiv.org/abs/2403.09532
We consider the problem of sampling from a high-dimensional target distribution $\pi_\beta$ on $\mathbb{R}^d$ with density proportional to $\theta\mapsto e^{-\beta U(\theta)}$ using explicit numerical schemes based on discretising the Langevin stocha
Externí odkaz:
http://arxiv.org/abs/2207.02600