The seminars will take place on Fridays 1 pm UK time in room MB0.08 in a hybrid format.

2021-2022 Organisers: Alice CorbellaLink opens in a new window and Lyudmila GrigoryevaLink opens in a new window

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

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)

2021/22 Term 3

The list of firmly confirmed speakers.

Date	Speaker	Title	F2F	Slides	Video
week 1 29/04	No seminar
week 2 06/05	Sinho ChewiLink opens in a new window	Improved dimension dependence for MALA and lower bounds for sampling
	Abstract: The optimal scaling literature predicts that the mixing time of the Metropolis-adjusted Langevin Algorithm (MALA) scales as d^{1/3}, where d is the dimension. However, the scaling limit requires stringent assumptions and is asymptotic in nature. In this work, we improve the state-of-the-art non-asymptotic mixing time bound for MALA on the class of log-smooth and strongly log-concave distributions from O(d) to O(d^{1/2}), under the additional assumption of a warm start; moreover, our bound is sharp. Our proof introduces a new technique based on a projection characterization of the Metropolis adjustment which reduces the study of MALA to the discretization analysis of the Langevin SDE. Afterwards, I will briefly discuss the elusive problem of obtaining lower bounds for sampling, including recent work which establishes the first tight complexity bound for sampling in one dimension. This is joint work with Kwangjun Ahn, Xiang Cheng, Patrik Gerber, Thibaut Le Gouic, Chen Lu, and Philippe Rigollet.
week 3 13/05	Simon SpencerLink opens in a new window	Accelerating adaptation in MCMC algorithms
	Abstract: The Metropolis-Hastings random walk algorithm remains popular with practitioners due to the wide variety of situations in which it can be successfully applied and the extreme ease with which it can be implemented. Adaptive versions of the algorithm use information from the early iterations of the Markov chain to improve the efficiency of the proposal. In this talk I will describe how to reduce the number of iterations needed to adapt the proposal to the target, which is particularly important when the likelihood is time-consuming to evaluate. First, the accelerated shaping algorithm is a generalisation of both the adaptive proposal and adaptive Metropolis algorithms. It is designed to remove misleading information from the estimate of the covariance matrix of the target. Second, the accelerated scaling algorithm rapidly changes the scale of the proposal to achieve a target acceptance rate. Finally, I will show how the same ideas can be applied to efficiently update parameter vectors with Dirichlet priors.
week 4 20/05	Sida ChenLink opens in a new window	Bayesian spline-based hidden Markov models with applications to activity acceleration data and sleep analysis
	Abstract: B-spline-based hidden Markov models use B-splines to specify the emission distributions and offer a more flexible modelling approach to data than conventional parametric HMMs. We introduce a Bayesian framework for inference in these models where the number of states may be unknown along with other model parameters. A trans-dimensional Markov chain inference algorithm is proposed to identify a parsimonious knot configuration of the B-splines while model selection regarding the number of states can be performed based on the marginal likelihood within a parallel sampling framework. Using an extensive simulation study, we establish the superiority of our proposed methodology in comparison to alternative approaches. We will also present an extension of the basic model - a novel hierarchical conditional HMM to analyse human accelerator activity data for circadian and sleep modelling.
week 5 27/05	Lucia PaciLink opens in a new window
week 6 3/06	BANK HOLIDAY
week 7 10/06	Gareth Roberts	Biased Draws and Corrections
	Abstract: Draws for major sporting events are often televised and carried out in a sequential fashion to maximise excitement and to increase anticipation for the sporting event itself. In this regard, organisations such as FIFA and UEFA in football have been highly successful. However these draw procedures are also often subject to constraints which make the problem of simulating a fair draw (ie uniform over all feasible draws which satisfy the constraints) difficult to achieve using a sequential procedure. For example the recent FIFA World Cup draw imposed geographical constraints to avoid countries from the same continental confederation (apart from Europe) playing each other in the group stages. Recent draws by FIFA and UEFA have all (to a greater or lesser extent) been biased. From a computational statistical perspective, they have been based on approximate SMC procedures. This talk will investigate these biases and suggest practical solutions which respect the desire to unveil such a draw in a sequential fashion. The main focus will be on the FIFA 2022 World Cup draw. This is joint work with Jeffrey RosenthalLink opens in a new window
week 8 17/06	Badr-Eddine Chérief-AbdellatifLink opens in a new window
week 9 24/06	Mauro Camara-EscuderoLink opens in a new window
week 10 1/7	End of year BBQ

The list of confirmed speakers. For abstracts please see the Programme PageLink opens in a new window.

Date	Speaker	Title		F2F	Slides	Video
Week 1 14/01	Ryan MartinLink opens in a new window	Data-driven Calibration of Generalized Posterior Distributions
Week 2 21/01	No seminar
Week 3 28/01	Filippo Pagani	Numerical Zig-Zag and Perturbation Bounds on Numerical Error
Week 4 04/02	Emilia Pompe	Robust Inference using Posterior Bootstrap
Week 5 11/02	Benedict Leimkuhler	Partitioned Integrators and Multirate Training of Deep Neural Networks
Week 6 18/02	Laura Guzman RinconLink opens in a new window	Bayesian estimation of the instant growth rate of SARS-CoV-2 positive cases in England, using Gaussian processes.
Week 7 25/02	Martyn Plummer	Developing JAGS
Week 8 04/03	Kamran PentlandLink opens in a new window	GParareal: A time-parallel ODE solver using Gaussian process emulation
Week 9 11/03	No Seminar
Week 10 18/03	Ioannis KosmidisLink opens in a new window

The list of confirmed speakers. For abstracts please see the Programme PageLink opens in a new window.

The list of confirmed speakers. For abstracts please see the Programme PageLink opens in a new window.

Date	Speaker	Title		F2F	Slides	Video
Week 1 14/01	Ryan MartinLink opens in a new window	Data-driven Calibration of Generalized Posterior Distributions
Week 2 21/01	No seminar
Week 3 28/01	Filippo Pagani	Numerical Zig-Zag and Perturbation Bounds on Numerical Error
Week 4 04/02	Emilia Pompe	Robust Inference using Posterior Bootstrap
Week 5 11/02	Benedict Leimkuhler	Partitioned Integrators and Multirate Training of Deep Neural Networks
Week 6 18/02	Laura Guzman RinconLink opens in a new window	Bayesian estimation of the instant growth rate of SARS-CoV-2 positive cases in England, using Gaussian processes.
Week 7 25/02	Martyn Plummer	Developing JAGS
Week 8 04/03	Kamran PentlandLink opens in a new window	GParareal: A time-parallel ODE solver using Gaussian process emulation
Week 9 11/03	No Seminar
Week 10 18/03	Ioannis KosmidisLink opens in a new window

Previous Years:

2020/2021Link opens in a new window

2019/2020

2018/2019

2017/2018

2016/2017

2015/2016

2014/2015

2013/2014

2012/2013

2011/2012 

2010/2011

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