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Gradient-based Adaptive Markov Chain Monte Carlo

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If you have a question about this talk, please contact Prof. Ramji Venkataramanan.

We introduce a gradient-based learning method to automatically adapt Markov chain Monte Carlo (MCMC) proposal distributions to intractable targets. We define a maximum entropy regularised objective function, referred to as generalised speed measure, which can be robustly optimised over the parameters of the proposal distribution by applying stochastic gradient optimisation. An advantage of our method compared to traditional adaptive MCMC methods is that the adaptation occurs even when candidate state values are rejected. This is a highly desirable property of any adaptation strategy because the adaptation starts in early iterations even if the initial proposal distribution is far from optimum. We apply the framework for learning multivariate random walk Metropolis and Metropolis-adjusted Langevin proposals with full covariance matrices, and provide empirical evidence in high dimensional targets that our method can outperform other MCMC algorithms, including Hamiltonian Monte Carlo schemes.

Bio: Michalis Titsias received a Diploma in Informatics from the University of Ioannina, Greece, in 1999, an MSc degree also from the University of Ioannina, in 2001, and a PhD degree from the School of Informatics, University of Edinburgh, in 2005. From October 2007 to July 2011, he worked as a research associate in the machine learning and optimisation research group at the School of Computer Science of the University of Manchester, while from November 2011 to September 2012 he worked as a postdoctoral research scientist in statistical cancer genomics at the Wellcome Trust Centre for Human Genetics and the Department of Statistics at the University of Oxford. From 2012 to 2018 was firstly a Lecturer, and later an Assistant Professor, in the Department of Informatics of the Athens University of Economics and Business, Greece. From October 2018, he works as a full time Research Scientist at DeepMind in London, UK. His research interests include machine learning, deep learning, reinforcement learning, data science and Bayesian statistics.

This talk is part of the Signal Processing and Communications Lab Seminars series.

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