• Corpus ID: 221103631

Non-convex Learning via Replica Exchange Stochastic Gradient MCMC

@article{Deng2020NonconvexLV,
  title={Non-convex Learning via Replica Exchange Stochastic Gradient MCMC},
  author={Wei Deng and Qi Feng and Liyao (Mars) Gao and Faming Liang and Guang Lin},
  journal={Proceedings of machine learning research},
  year={2020},
  volume={119},
  pages={
          2474-2483
        }
}
Replica exchange Monte Carlo (reMC), also known as parallel tempering, is an important technique for accelerating the convergence of the conventional Markov Chain Monte Carlo (MCMC) algorithms. However, such a method requires the evaluation of the energy function based on the full dataset and is not scalable to big data. The naïve implementation of reMC in mini-batch settings introduces large biases, which cannot be directly extended to the stochastic gradient MCMC (SGMCMC), the standard… 

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