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Poster
Replica-Exchange Nos\'e-Hoover Dynamics for Bayesian Learning on Large Datasets
Rui Luo · Qiang Zhang · Yaodong Yang · Jun Wang

Thu Dec 10 09:00 AM -- 11:00 AM (PST) @ Poster Session 5 #1608

In this paper, we present a new practical method for Bayesian learning that can rapidly draw representative samples from complex posterior distributions with multiple isolated modes in the presence of mini-batch noise. This is achieved by simulating a collection of replicas in parallel with different temperatures and periodically swapping them. When evolving the replicas' states, the Nos\'e-Hoover dynamics is applied, which adaptively neutralizes the mini-batch noise. To perform proper exchanges, a new protocol is developed with a noise-aware test of acceptance, by which the detailed balance is reserved in an asymptotic way. While its efficacy on complex multimodal posteriors has been illustrated by testing over synthetic distributions, experiments with deep Bayesian neural networks on large-scale datasets have shown its significant improvements over strong baselines.

Author Information

Rui Luo (University College London)
Qiang Zhang (University College London)
Yaodong Yang (University College London)
Jun Wang (JD AI Research & UCL)

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