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Algorithms & Computationally Intensive Inference seminars

2020/2021: The seminars will happen on Microsoft Teams on Fridays 1pm UK time (or occasionally at different times).

If you are not affiliated with Warwick and wish to attend our seminars please register here.

If you would like to speak, or you want to be included in any emails, please contact one of the organisers.

Current Organisers: Alice Corbella, Massimiliano Tamborrino, Jure Vogrinc

Website URL: www.warwick.ac.uk/compstat

Mailing List Sign-Up: http://mailman1.csv.warwick.ac.uk/mailman/listinfo/algorithmseminar

Mailing List: algorithmseminar@listserv.csv.warwick.ac.uk (NB - only approved members can post)

Microsoft Teams Link (username/password same as for eduroam) available here

Next Talk: Friday June 25th, 1pm UK time

Sophie Langer: Deep Learning meets statistics: Improving neural networks with statistical theory

Abstract: In this talk we analyse the L2 error of neural network regression estimates with one hidden layer. Under the assumption that the Fourier transform of the regression function decays suitably fast, we show that an estimate, where all initial weights are chosen according to proper uniform distributions and where the weights are learned by gradient descent, achieves a rate of convergence of 1/ n (up to a logarithmic factor). Our statistical analysis implies that the key aspect behind this result is the proper choice of the initial inner weights and the adjustment of the outer weights via gradient descent. This indicates that we can also simply use linear least squares to choose the outer weights. We prove a corresponding theoretical result and compare our new linear least squares neural network estimate with standard neural network estimates via simulated data. Our simulations show that our theoretical considerations lead to an estimate with an improved performance. Hence the development of statistical theory can indeed improve neural network estimates.

2020/21 Term 3:

Date Speaker Title Abstract Slides Video
Week 1: 30/4 Jure Vogrinc Counterexamples for optimal scaling of Metropolis-Hastings chains with rough target densities Abstract Slides Video
Week 2: 7/5 Gilles Louppe The frontiers of simulation-based inference Abstract   Video
Week 3: 14/5 Andrea Bertazzi Euler approximations for Piecewise deterministic Markov processes Abstract   Video
Week 4: 21/5 Juan Kuntz Nussio Product-form estimators: exploiting independence to scale up Monte Carlo Abstract    
Week 5: 28/5 Dootika Vats The new BFFs in MCMC: Barker and Bernoulli Abstract   Video
Week 6: 4/6 Anthony Lee A general perspective on the Metropolis-Hastings kernel: incorporating stopping times in proposals. Abstract Slides Video
Week 7: 11/6 Timothée Stumpf-Fétizon Exact Bayesian Inference for Markov Switching Diffusions Abstract Slides Video
Week 8: 18/6 Helen Ogden

Information criteria for model choice in finite mixture models

Abstract    
Week 9: 25/6 Sophie Langer Deep Learning meets statistics: Improving neural networks with statistical theory Abstract    
Week 10: 2/7 CANCELLED DUE TO ISBA MEETING      

2020/21 Term 2:

Date Speaker Title Abstract Slides Video
Week 1: 15/1 (4pm UK time) Jeremias Knoblauch Postponed Abstract    
Week 2: 22/1 (4pm UK time) Jeffrey Rosenthal MCMC Confidence Intervals and Biases Without CLTs Abstract Slides Video
Week 3: 29/1 Paul Dobson Subgeometric hypocoercivity for piecewise-deterministic Markov process Monte Carlo methods Abstract Slides Video
Week 4: 5/2 (4:30 UK time) Daniel Jerison MCMC convergence bounds for reversible chains Abstract Slides Video
Week 5: 12/2 Jaromir Sant Inference of natural selection from allele frequency time series data using exact simulation techniques Abstract Slides Video
Week 6: 19/2 Xenia Miscouridou

Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data

Abstract   Video
Week 7: 26/2 Nianqiao (Phyllis) Ju Sequential Monte Carlo algorithms for agent-based models of disease transmission Abstract Slides Video
Week 8: 5/3 Adeline Samson
Computational statistics for neuronal mathematical models
Abstract Slides Video
Week 9: 12/3 (4 pm UK time) Ari Stern Structure-preserving numerical integrators: classic methods, new perspectives Abstract   Video
Week 10: 19/3 Jeremias Knoblauch Optimization-centric Generalizations of Bayesian Inference Abstract Slides Video
2020/21 Term 1 (and September)

This is the list of confirmed speakers, which will be continuously updated.

Date Speaker Title Abstract Slides Video
Week -2: 18/09 (12 UK time) Clara Grazian Approximate Bayesian analysis of (un)conditional copulas Abstract Slides Video
Week -1: 25/09 Joe Meagher Bayesian Ancestral Reconstruction for Bat Echolocation Abstract Slides Video
Week0: 02/10 Michael Choi On the convergence of an improved and adaptive kinetic simulated annealing Abstract Slides Video
Week 1: 09/10   cancelled (OxWaSP workshop)      
Week 2: 16/10 (2pm UK time) Liangliang Wang Sequential Monte Carlo for estimating parameters of differential equations Abstract Slides Video
Week 3: 23/10 Sebastian Vollmer

Part 1: Risk prediction and Risk prediction

Part 2: Machine Learning in Julia and benchmarking results on predictive fairness.

Abstract   Video
Week 4: 30/10 (4pm UK time) Philippe Gagnon Lifted samplers for partially ordered discrete state-spaces Abstract   Video
Week 5: 6/11 Richard Everitt

Rare event ABC-SMC^2

Abstract Slides Video
Week 6: 13/11 Letizia Angeli Interacting Particle Systems Approximations of Feynman-Kac Formulae in continuous time Abstract Slides Video
Week 7: 20/11 Yan Qu Exact Simulation of Self-Excited Point Process with Levy Driven OU Abstract   Video
Week 8: 27/11
(4pm UK time)
Caroline Colijn
COVID-19 data sources and their challenges for modelling and estimation
Abstract   Video
Week 9: 4/12 Suzie Brown Asymptotic genealogies of sequential Monte Carlo algorithms Abstract   Video
Week 10: 11/12   cancelled      

Previous Years:

2019/2020

2018/2019

2017/2018

2016/2017

2015/2016

2014/2015

2013/2014

2012/2013

2011/2012 

2010/2011

Some key phrases:

- Sampling and inference for diffusions
- Exact algorithms
- Intractable likelihood
- Pseudo-marginal algorithms
- Particle filters
- Importance sampling
- MCMC
- Adaptive MCMC
- Perfect simulation
- Markov chains
- Random structures
- Randomised algorithms