Turing/LMS Workshop

Inverse Problems and Data Science

8-10 May 2017

Venue: Informatics Forum, 10 Crichton St, Edinburgh EH8 9AB, UK

Organisers: Natalia Bochkina (University of Edinburgh), Carola Schoenlieb (University of Cambridge), Marta Betcke (UCL), Sean Holman (University of Manchester)

The aim of the workshop is to bring together researchers on inverse problems working in different areas of mathematics, statistics and machine learning as well as from the applied disciplines where inverse problems arise, such as astronomy, biology, computer vision, geoscience and medicine. The topics of the workshop include nonlinear inverse problems, algorithms, inverse problems in machine learning, theoretical properties of statistical estimators in inverse problems, Bayesian inverse problems, applications in science and medicine.

Financial support: The Alan Turing Institute and London Mathematical Society

Sponsoring: if you would like to sponsor the event, please get in touch with one of the organisers. We are grateful to Schlumberger for sponsorship.

 

Times: the workshop will start at 10am on Monday 8th of May and concludes at 4:30pm on Wednesday 10th of May

Contact: events@turing.ac.uk

 

Registration: deadline is 30 April, registration fee is £60. Please register here.

Poster abstract submission deadline: Friday 7th of April. The decision will be made by 14th of April

To submit the poster, please send your name, institution, title and abstract (indicate if you are open to giving a talk) to edinburgh.workshop.stats@gmail.com.

We have a small number of travel bursaries available for UK-based PhD students presenting a poster – please indicate if you wish to apply when submitting poster abstract. 

 

There will also be a training course on Bayesian inverse problems on 11 May 2017 (link) – registration will be available soon.

Confirmed speakers:

Marta Betcke (Computer Science, UCL, UK) “Dynamic high-resolution photoacoustic tomography with optical flow constraint

Michal Branicki (Mathematics, University of Edinburgh, UK)

Mike Christie (Petroleum Institute, Heriot Watt University, UK) ”Bayesian Hierarchical Models for Measurement Error

Christian Clason (Mathematics, Duisburg-Essen University, Germany)” Functional error estimators for the adaptive discretization of inverse problem

Andrew Curtis (GeoSciences, University of Edinburgh, UK)

Sean Holman (Mathematics, University of Manchester, UK) “On the stability of the geodesic ray transform in the presence of caustics

Michael Gutmann (Informatics, University of Edinburgh, UK)” Bayesian Inference by Density Ratio Estimation

Kyong Jin (EPFL, Switzerland) “Deep Convolutional Neural Network for Inverse Problems in Imaging

Axel Munk (Goettingen University, Germany) Nanostatistics – Statistics for Nanoscopy

Gabriel Paternain (University of Cambridge, UK) ”Effective inversion of the attenuated X-ray transform associated with a connection”

Marcelo Pereyra (Mathematics, Heriot Watt University, UK) ”Bayesian inference by convex optimisation: theory, methods, and algorithms.

Markus Reiss (Humboldt University, Berlin, Germany)” Optimal adaptation for early stopping in statistical inverse problems

Carola Schoenlieb (University of Cambridge) “Model-based learning in imaging”

Anna Simoni (CREST, CNRS, Paris, France) ”Nonparametric Estimation in case of Endogenous Selection”

Botond Szabo (Leiden University, Netherlands) ”Confidence in Bayesian uncertainty quantification in inverse problems

Aretha Teckentrup (Mathematics, University of Edinburgh, UK)” Gaussian process regression in Bayesian inverse problems”

Nicholas Zabaras (Computational Science and Engineering, University of Notre Dame, USA) “Inverse Problems with an Unknown Scale of Estimation

 

Abstracts.

Christian Clason (Mathematics, Duisburg-Essen University, Germany)” Functional error estimators for the adaptive discretization of inverse problem

This talk discusses the application of functional error estimators for the adaptive discretization of inverse problems for partial differential equations. The error estimators can be written in terms of residuals in the optimality system that can then be estimated by conventional techniques, thus leading to explicit estimators.

This approach is in particular well-suited for inverse problems in Banach spaces, which involve non-smooth penalties such as sparsity enhancement or pointwise penalties.

 

Mike Christie (Petroleum Institute, Heriot Watt University, UK) ”Bayesian Hierarchical Models for Measurement Error

The detailed geological description of oil reservoirs is always uncertain because of the large size and relatively small number of wells from which hard data can be obtained.  To handle this uncertainty, reservoir models are calibrated or ‘history matched’ to production data (oil rates, pressures etc). The quality of any reservoir forecasts depends not only on the quality of the match, but also how well understood the measurement errors are (or indeed the split between measurement and modelling errors).

This talk will look at hierarchical models for estimating measurement and modelling errors in reservoir model calibration, and compare maximum likelihood estimates of measurement errors with marginalisation over unknown errors.

Michael Gutmann (Informatics, University of Edinburgh, UK)” Bayesian Inference by Density Ratio Estimation”

This talk is about Bayesian inference when the likelihood function cannot be computed but data can be generated from the model. The model's data generating process is allowed to be arbitrarily complex. Exact solutions are then not possible. But by re-formulating the original problem as a problem of estimating the ratio between two probability density functions, I show how e.g. logistic regression can be used to obtain approximate solutions. The proposed inference framework is illustrated on stochastic nonlinear dynamical models.

Reference: https://arxiv.org/abs/1611.10242

Kyong Jin (EPFL, Switzerland) “Deep Convolutional Neural Network for Inverse Problems in Imaging

This talk discusses a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyper parameter selection. The starting point of our work is the observation that unrolled iterative methods have the form of a CNN (filtering followed by point-wise non-linearity) when the normal operator (H*H, the adjoint of H times H) of the forward model is a convolution. Based on this observation, we propose using direct inversion followed by a CNN to solve normal-convolutional inverse problems. The direct inversion encapsulates the physical model of the system, but leads to artifacts when the problem is ill-posed; the CNN combines multiresolution decomposition and residual learning in order to learn to remove these artifacts while preserving image structure. The performance of the proposed network will be demonstrated in sparse-view reconstruction on parallel beam X-ray computed tomography and accelerated MR imaging reconstruction on parallel MRI.

Axel Munk (Department of Mathematics and Computer Science, and Max-Planck Institute for Biophysical Chemistry, Goettingen University, Germany)

Nanostatistics – Statistics for Nanoscopy

Conventional light microscopes have been used for centuries for the study of small length scales down to approximately 250 nm. Images from such a microscope are typically blurred and noisy, and the measurement error in such images can often be well approximated by Gaussian or Poisson noise. In the past, this approximation has been the focus of a multitude of deconvolution techniques in imaging. However, conventional microscopes have an intrinsic physical limit of resolution. Although this limit remained unchallenged for a century, it was broken for the first time in the 1990s with the advent of modern superresolution fluorescence microscopy techniques. Since then, superresolution fluorescence microscopy has become an indispensable tool for studying the structure and dynamics of living organisms, recently acknowledged with the c Nobel prize in chemistry 2014. Current experimental advances go to the physical limits of imaging, where discrete quantum effects are predominant. Consequently, the data is inherently of a non-Gaussian statistical nature, and we argue that recent technological progress also challenges the long-standing Poisson assumption. Thus, analysis and exploitation of the discrete physical mechanisms of fluorescent molecules and light, as well as their distributions in time and space, have become necessary to achieve the highest resolution possible and to extract biologically relevant information.

In this talk we survey some modern fluorescence microscopy techniques from a statistical modeling and analysis perspective. In the first part we address spatially adaptive multiscale deconvolution estimation and testing methods for scanning type microscopy. We illustrate that such methods benefit from recent advances in large-scale computing, mainly from convex optimization. In the second part of the talk we address challenges of quantitative biology which require more detailed models that delve into sub-Poisson statistics. To this end we suggest a prototypical model for fluorophore dynamics and use it to quantify the number of proteins in a spot.

Marcelo Pereyra (Mathematics, Heriot Watt University, UK) ”Bayesian inference by convex optimisation: theory, methods, and algorithms.

Convex optimisation has become the main Bayesian computation methodology in many areas of data science such as mathematical imaging and machine learning, where high dimensionality is often addressed by using models that are log-concave and where maximum-a-posteriori (MAP) estimation can be performed efficiently by optimisation. The first part of this talk presents a new decision-theoretic derivation of MAP estimation and shows that, contrary to common belief, under log-concavity MAP estimators are proper Bayesian estimators. A main novelty is that the derivation is based on differential geometry. Following on from this, we establish universal theoretical guarantees for the estimation error involved and show estimation stability in high dimensions. Moreover, the second part of the talk describes a new general methodology for approximating Bayesian high-posterior-density regions in log-concave models.  The approximations are derived by using recent concentration of measure results related to information theory, and can be computed very efficiently, even in large-scale problems, by using convex optimisation techniques. The approximations also have favourable theoretical properties, namely they outer-bound the true high-posterior-density credibility regions, and they are stable with respect to model dimension. The proposed methodology is finally illustrated on two high-dimensional imaging inverse problems related to tomographic reconstruction and sparse deconvolution, where they are used to explore the uncertainty about the solutions, and where convex-optimisation-empowered proximal Markov chain Monte Carlo algorithms are used as benchmark to compute exact credible regions and measure the approximation error.

Related pre-prints:

https://arxiv.org/abs/1612.06149

https://arxiv.org/pdf/1602.08590.pdf

Markus Reiss (Humboldt University, Berlin, Germany)” Optimal adaptation for early stopping in statistical inverse problems

For linear inverse problems $Y=\mathsf{A}\mu+\xi$, it is classical to recover the unknown function $\mu$ by an iterative scheme $(\widehat \mu^{(m)}, m=0,1,\ldots)$ and to provide $\widehat\mu^{(\tau)}$ as a result, where $\tau$ is some stopping rule. Stopping should be decided adaptively, that is in a data-driven way independently of the true function $\mu$.  For deterministic noise $\xi$  the discrepancy principle is usually applied to determine $\tau$. In the context of  stochastic noise $\xi$, we study oracle adaptation (that is, compared to the best possible stopping iteration). For a stopping rule based on the residual process, oracle adaptation bounds within a certain domain are established.  For Sobolev balls, the domain of adaptivity matches a corresponding lower bound. The proofs use bias and variance transfer techniques from weak prediction error to strong $L^2$-error, as well as convexity arguments and concentration bounds for the stochastic part. The performance of our stopping rule for Landweber and spectral cutoff methods is illustrated numerically.(Joint work with Gilles Blanchard, Potsdam, and Marc Hoffmann, Paris)

Anna Simoni (CREST, CNRS, Paris, France) Nonparametric Estimation in case of Endogenous Selection”

This paper addresses the problem of estimation of a nonparametric regression function from selectively observed data when selection is endogenous. Our approach relies on independence between covariates and selection conditionally on potential outcomes. Endogeneity of regressors is also allowed for. In the exogenous and endogenous case, consistent two-step estimation procedures are proposed and their rates of convergence are derived which take into account the degree of ill-posedness. In the first stage we have to solve an ill-posed inverse problem to recover nonparametrically the inverse selection probability function. Moreover, when the covariates are endogenous an additional inverse problem has to be solved in the second step to recover the instrumental regression function. Pointwise asymptotic distribution of the estimators is established. In addition, bootstrap uniform confidence bands are derived. Finite sample properties are illustrated in a Monte Carlo simulation study and an empirical illustration. Joint work with Christoph Breunig (Humboldt University, Berlin) and Enno Mammen (Heidelberg University).

Botond Szabo (Leiden University, Netherlands) ”Confidence in Bayesian uncertainty quantification in inverse problems

In our work we investigate the frequentist coverage of Bayesian credible sets in the inverse Gaussian sequence model. We consider a scale of priors of varying regularity and choose the regularity by an empirical or a hierarchical Bayes method. Next we consider a central set of prescribed posterior probability in the posterior distribution of the chosen regularity. We show that such an adaptive Bayes credible set gives correct uncertainty quantification of “polished tail” parameters, in the sense of high probability of coverage of such parameters. On the negative side, we show by theory and example that adaptation of the prior necessarily leads to gross and haphazard uncertainty quantification for some true parameters that are still within the hyperrectangle regularity scale. The preceding results are based on semi-explicit computations on an optimised statistical model. In the end of the talk I will briefly discuss to possible extensions of our coverage results to more general, abstract settings.

The talk is based on the papers written together with Judith Rousseau, Aad van der Vaart and Harry van Zanten.

 

Aretha Teckentrup (Mathematics, University of Edinburgh, UK)” Gaussian process regression in Bayesian inverse problems”

A major challenge in the application of sampling methods in Bayesian inverse problems is the typically large computational cost associated with solving the forward problem. To overcome this issue, we consider using a Gaussian process surrogate model to approximate the forward map. This results in an approximation to the solution of the Bayesian inverse problem, and more precisely in an approximate posterior distribution.

In this talk, we analyse the error in the approximate posterior distribution, and show that the approximate posterior distribution tends to the true posterior as the accuracy of the Gaussian process surrogate model increases.

 

Nicholas Zabaras (Computational Science and Engineering, University of Notre Dame, USA) “Inverse Problems with an Unknown Scale of Estimation

The presentation will focus on the Bayesian estimation of spatially varying parameters of multiresolution/multiscale nature. In particular, the characteristic length scale(s) of the unknown property are not known a priori and need to be evaluated based on the fidelity of the given data across the domain. Our approach is based on representing the spatial field with a wavelet expansion. The intra-scale correlations between wavelet coefficients form a quadtree, and this structure is exploited to identify additional basis functions to refine the model. Bayesian inference is performed using a sequential Monte Carlo sampler with a MCMC transition kernel. The SMC sampler is used to move between posterior densities defined on different scales, thereby providing for adaptive refinement of the wavelet representation. The marginal likelihoods provide a termination criterion for the scale determination algorithm thus allowing model comparison and selection. The approach is demonstrated with permeability estimation for groundwater flow using pressure measurements.

https://www.zabaras.com/

Link to the LMS Inverse Problems Research Group