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Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families

Strathmann, H; Sejdinovic, D; Livingstone, S; Szabo, Z; Gretton, A; (2015) Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families. In: Cortes, C and Lawrence, ND and Lee, DD and Sugiyama, M and Garnett, R, (eds.) Advances in Neural Information Processing Systems 28 (NIPS 2015). NIPS Proceedings Green open access

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Abstract

We propose Kernel Hamiltonian Monte Carlo (KMC), a gradient-free adaptive MCMC algorithm based on Hamiltonian Monte Carlo (HMC). On target densities where classical HMC is not an option due to intractable gradients, KMC adaptively learns the target's gradient structure by fitting an exponential family model in a Reproducing Kernel Hilbert Space. Computational costs are reduced by two novel efficient approximations to this gradient. While being asymptotically exact, KMC mimics HMC in terms of sampling efficiency, and offers substantial mixing improvements over state-of-the-art gradient free samplers. We support our claims with experimental studies on both toy and real-world applications, including Approximate Bayesian Computation and exact-approximate MCMC.

Type: Proceedings paper
Title: Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families
Event: Neural Information Processing Systems 2015
Open access status: An open access version is available from UCL Discovery
Publisher version: https://papers.nips.cc/paper/5890-gradient-free-ha...
Language: English
Additional information: Copyright © The Authors 2015.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/1496386
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