Statistical Learning & Inference Seminars
The seminars will take place every Tuesday 11am-12pm during term time.
Special Pre-term seminar
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24/09, 11am, B3.02 (Zeeman) |
Botond Szabo (Bocconi)Link opens in a new window | Vecchia approximation for Deep Gaussian Processes | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: | Deep Gaussian processes have several advantages compared to standard Gaussian Processes (GPs), including learning local and compositional structures of the signal. However, their practical applicability is hindered by their computational complexity and instability of approximation methods. In this work we propose a novel Vecchia approximation of Deep GPs, derive optimal contraction rates for structured (compositional) functions and discuss algorithmic aspects. We also demonstrate the applicability of the proposed method on synthetic data sets. Based on an ongoing joint work with Ismael Castillo (Sorbone, Paris), Thibault Randrianarisoa (Vector Institute, Toronto) and Yichen Zhou (University of Hong Kong). | |||||||||||||||||||||||||||||||||||||||||||||||||||
Term 1, 26-27
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| 06/10, 11am, MB2.22 | Andrew Yiu (Southampton)Link opens in a new window | Approximating causal effects under positivity violations | ||||||||||||||||||||||||||||||||||
| Abstract: | Positivity violations constitute one of the two principal challenges of causal inference from observational data, alongside unmeasured confounding. The positivity assumption requires every unit to have a strictly positive probability of receiving each treatment, conditional on the covariates used for adjustment. When positivity is violated, there are regions of the covariate space in which no outcome information is available under one of the treatments. In the most serious case, the target causal effect is not identifiable from the observed data, leaving us either to change the target estimand or to rely on untestable extrapolation assumptions. We introduce a new method called "overlap approximation", which produces a class of intermediate estimands that progressively approach the target while remaining identifiable and efficiently estimable under positivity violations. We discuss uniform semiparametric inference for these classes, including the use of bootstrapping to construct confidence bands and tests. | |||||||||||||||||||||||||||||||||||
| 13/10, 11am, MB2.22 | Vaidehi Dixit (Nottingham)Link opens in a new window | Anytime valid and asymptotically optimal statistical inference driven by predictive recursion | ||||||||||||||||||||||||||||||||||
| Abstract: | Distinguishing two classes of candidate models is a fundamental and practically important problem in statistical inference. Error rate control is crucial to the logic but, in complex nonparametric settings, such guarantees can be difficult to achieve, especially when the stopping rule that determines the data collection process is not available. My talk is based on construction of e-processes for comparison of such models in a quasi-Bayesian setting. A particular novel e-process construction by us leverages the so-called predictive recursion (PR) algorithm. The proposal is based on constructing a marginal likelihood by mixing over a specified class of distributions. Such a likelihood could be constructed in a Bayesian way by introducing a prior on the class of distributions in the alternative and finding the corresponding Bayesian marginal likelihood. But implementing a purely Bayesian strategy to account for nonparametric aspects of applications can be computationally demanding. The PR algorithm stems as an approximation to the posterior mean of the mixing distribution under the Dirichlet process prior and hence is able to rapidly and recursively fit nonparametric mixture models. The resulting PRe-process affords anytime valid inference uniformly over stopping rules and is shown to be efficient in the sense that it achieves the maximal growth rate under the alternative relative to the mixture model being fit by PR. I will also explore some applications in spatio-temporal inference. | |||||||||||||||||||||||||||||||||||
| 20/10, 11am, MB2.22 | Matt Nunes (Bath)Link opens in a new window | Multiscale dynamic dependence estimation over networks | ||||||||||||||||||||||||||||||||||
| Abstract: | Many multivariate time series datasets naturally exhibit nonstationary characteristics. In addition, in many applications the data are associated to a network structure, and thus analysis tools should incorporate dependence which is restricted to this structure. In this work, we propose a new modelling framework for data arising on networks which capture data nonstationarities. We introduce the concept of a *local* partial correlation graph, which formalises the lack of dependence between components of the series to non-edges in the time-varying inverse wavelet spectrum, and allows us to estimate model quantities consistently via a wavelet-spectral graphical modelling approach. We illustrate our proposed model on data arising from assets in a network of banking institutions. | |||||||||||||||||||||||||||||||||||
| 27/10, 11am, MB2.22 | Semiparametric regression for circular response | |||||||||||||||||||||||||||||||||||
| Abstract: | In standard settings, with real-valued random variables, there are many tools to estimate conditional mean, mode, quantiles, variance functions, ranging from parametric, semiparametric and nonparametric procedures (depending on realistic assumptions that can be justified or not). In many applications the variable of interest, the response, is circular, for example flight orientation of migrating birds, ocean current direction, orientation of geological fractures, orientation of proteins in structural biology. Possible predicting variables can be real-valued (flight altitude of birds, windspeed, ...) or also of circular type (e.g. wind direction). Moreover, predicting factors can have an influence not only on centrality measures (mean direction, mode, quantiles, ...) but also on the variability (variance, dispersion, concentration, ....). Motivated by a study on flight orientations of migrating birds, using parametric models, and facing limitations of these, we propose a method to estimate semiparametrically the conditional modal direction as well as conditional concentration. We introduce the semiparametric regression model, and present results for statistical inference. The added value of the developed semiparametric method is illustrated in an ecology application. | |||||||||||||||||||||||||||||||||||
| 03/11, 11am, MB2.22 | ||||||||||||||||||||||||||||||||||||
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| 10/11, 11am, MB2.22 | Rowland Seymour (Birmingham)Link opens in a new window | Bayesian Computation for Spatial Comparative Judgement Models | ||||||||||||||||||||||||||||||||||
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Comparative judgement studies elicit quality assessments through pairwise comparisons, typically analysed using the Bradley–Terry model. We have developed a suite of spatial comparative judgement models and run studies to map the prevalence of crimes such as domestic abuse, modern slavery, and forced marriage. This talk describes work on extracting more information per comparison, organised around three extensions to the standard model. First, a multivariate normal prior distribution whose covariance matrix is built from the network of areas in the study. This lets neighbouring areas borrow strength and reduces the data required by around ninety per cent. Second, a Pólya-Gamma latent variable representation that yields a Gaussian full conditional while retaining that correlated prior distribution, which allows inference to be carried out in 20 to 30 seconds. Third, an experimental design that weights pairs by the prior variance they explain, made computable for large studies by a reduced basis decomposition of the associated pairs-of-pairs covariance matrix. We demonstrate these methods in several real world studies, describe the changes they have brought about in safeguarding practice, and outline our steps towards commercialising the work. | |||||||||||||||||||||||||||||||||||
| 17/11, 11am, MB2.22 | Sarah Heaps (Durham)Link opens in a new window | |||||||||||||||||||||||||||||||||||
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| 24/11, 11am, MB2.22 | Yunxiao Chen (LSE)Link opens in a new window | |||||||||||||||||||||||||||||||||||
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| 01/12, 11am, MB2.22 | ||||||||||||||||||||||||||||||||||||
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| 08/12, 11am, MB2.22 | ||||||||||||||||||||||||||||||||||||
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Term 2, 26-27
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12/01, 11am, MB0.08 |
Anders Kock (Oxford) |
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| 19/01, 11am, MB2.22 |
Kartik Waghmare (ETH Zurich) |
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| 26/01, 11am, MB2.22 | Alex Gibberd (Heriot-Watt) | ||||||||||||||||||||||||||||||||
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| 02/02, 11am, MB2.22 | Chao Zheng (Southampton) | ||||||||||||||||||||||||||||||||
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| 16/02, 11am, MB2.22 | Qingyuan Zhao (Cambridge) | ||||||||||||||||||||||||||||||||
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| 23/02, 11am, MB2.22 | Oliver Dukes (Ghent) TBC | ||||||||||||||||||||||||||||||||
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02/03, 11am, MB2.22 |
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Term 3, 26-27
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27/04, 11am MB0.07 |
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04/05, 11am MB0.07 |
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11/05, 11am MB0.07 |
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18/05, 11am MB0.07 |
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25/05, 11am MB0.07 |
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01/06, 11am MB0.07 |
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08/06, 11am MB0.07 |
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15/06, 11am MB0.07 |
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22/06, 11am MB0.07 |
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29/06, 11am MB0.07 |
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