All About That Bayes - Alexandre Bouchard-Côté on the 15th of March, 4 p.m.

[All About That Bayes]

Dear all,

On Tuesday 15th of March at 4 p.m., we will have the pleasure of welcoming Alexandre Bouchard-Côté, Associate Professor of Statistics at University of British Columbia (https://www.stat.ubc.ca/~bouchard/index.html).

The talk will take place at AgroParisTech (16 rue Claude Bernard), in the Amphitheatre Dumont (don't hesitate to ask at the main entrance to find the room).

Best regards,

The organising team

Title: Approximation of intractable integrals using non-reversibility and non-linear distribution paths

Abstract: In the first part of the talk, I will present an adaptive, non-reversible Parallel Tempering (PT) allowing MCMC exploration of challenging problems such as single cell phylogenetic trees. A sharp divide emerges in the behaviour and performance of reversible versus non-reversible PT schemes: the performance of the former eventually collapses as the number of parallel cores used increases whereas non-reversible benefits from arbitrarily many available parallel cores. These theoretical results are exploited to develop an adaptive scheme to efficiently optimize over annealing schedules. 

In the second half, I will talk about the global communication barrier, a fundamental limit shared by both reversible and non-reversible PT methods, and on our recent work that leverage non-linear annealing paths to provably and practically break that barrier. 

My group is also interested in making these advanced non-reversible Monte Carlo methods easily available to data scientists. To do so, we have designed a Bayesian modelling language to perform inference over arbitrary data types using non-reversible, highly parallel algorithms.

References:

• Non-Reversible Parallel Tempering: a Scalable Highly Parallel MCMC Scheme (2021). S. Syed, A. Bouchard-Côté, G. Deligiannidis, A. Doucet. Journal of Royal Statistical Society, Series B. https://rss.onlinelibrary.wiley.com/doi/10.1111/rssb.12464
• Parallel Tempering on Optimized Paths (2021). S. Syed, V. Romaniello, T. Campbell, A. Bouchard-Côté. International Conference on Machine Learning (ICML). http://proceedings.mlr.press/v139/syed21a/syed21a.pdf
• Software: Blang: Probabilitistic Programming for Combinatorial Spaces. A. Bouchard-Côté, K. Chern, D. Cubranic, S. Hosseini, J. Hume, M. Lepur, Z. Ouyang, G. Sgarbi. Journal of Statistical Software (Accepted). https://arxiv.org/abs/1912.10396, https://www.stat.ubc.ca/~bouchard/blang/