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Stochastic gradient  sg -based algorithms for Markov chain Monte Carlo sampling ( sgmcmc ) tackle large-scale Bayesian modeling problems by operating on mini-batches and injecting noise on  sg steps. The sampling properties of these algorithms are determined by user choices, such as the covariance of the injected noise and the learning rate, and by problem-specific factors, such as assumptions on the loss landscape and the covariance of  sg  noise. However, current  sgmcmc  algorithms applied to popular complex models such as Deep Nets cannot simultaneously satisfy the assumptions on loss landscapes and on the behavior of the covariance of the  sg  noise, while operating with the practical requirement of non-vanishing learning rates. In this work we propose a novel practical method, which makes the  sg  noise isotropic, using a fixed learning rate that we determine analytically. Extensive experimental validations indicate that our proposal is competitive with the state of the art on  sgmcmc .

A Scalable Bayesian Sampling Method Based on Stochastic Gradient Descent Isotropization