Approximation, sampling, and compression in high dimensional problems
Monday 17th June 2019 to Friday 21st June 2019
09:20 to 09:40  Registration  
09:40 to 09:50  Welcome from David Abrahams (Isaac Newton Institute)  
09:50 to 10:40 
Akram Aldroubi (Vanderbilt University); (Vanderbilt University) Dynamical sampling and frames generated from powers of exponential operators
In this talk, I will give a brief review of the problem of frame generation from operator powers of exponentials acting on a set of vectors. I will discuss its relation to dynamical sampling, review some of the previous results and present several new ones.

INI 1  
10:40 to 11:10  Morning Coffee  
11:10 to 12:00 
Denka Kutzarova (University of Illinois at UrbanaChampaign); (Bulgarian Academy of Sciences) Transportation cost spaces on finite metric spaces
Transportation cost spaces are studied by several groups of researchers, for different reasons and under different names. The term Lipschitzfree spaces is commonly used in Banach space theory. We prove that the transportation cost space on any finite metric space contains a large wellcomplemented subspace which is close to $\ell_1^n$. We show that transportation cost spaces on large classes of recursively defined sequences of graphs are not uniformly isomorphic to $\ell_1^n$ of the corresponding dimensions. These classes contain wellknown families of diamond graphs and Laakso graphs. In the particular case of diamond graphs we prove that their cycle space is spanned by even levels of Haar functions. It is curious that the subspaces generated by all the even/odd levels of the Haar functions also appear in the study of quasigreedy basic sequences in $L_1[0,1]$. This research is joint with Stephen Dilworth and Mikhail Ostrovskii. 
INI 1  
12:00 to 13:30  Lunch at Churchill College  
13:30 to 14:20 
Albert Cohen (Université Pierre et Marie Curie Paris) Optimal sampling for approximation on general domains
We consider the approximation of an arbirary function
in any dimension from point samples. Approximants are picked from given or adaptively chosen finite dimensional spaces. Various recent works reveal that optimal
approximations can be constructed at minimal sampling budget by leastsquares methods with particular sampling measures. In this talk, we discuss strategies to construct these measures and their samples in the adaptive context and in general nontensorproduct multivariate domains. 
INI 1  
14:20 to 15:10 
Peter Binev (University of South Carolina) High Dimensional Approximation via Sparse Occupancy Trees
Adaptive domain decomposition is often used in finite elements methods for solving partial differential equations in low space dimensions. The adaptive decisions are usually described by a tree. Assuming that can find the (approximate) error for approximating a function on each element of the partition, we have shown that a particular coarsetofine method provides a nearbest approximation. This result can be extended to approximating point clouds any space dimension provided that we have relevant information about the errors and can organize properly the data. Of course, this is subject to the curse of dimensionality and nothing can be done in the general case. In case the intrinsic dimensionality of the data is much smaller than the space dimension, one can define algorithms that defy the curse. This is usually done by assuming that the data domain is close to a low dimensional manifold and first approximating this manifold and then the function defined by it. A few years ago, together with Philipp Lamby, Wolfgang Dahmen, and Ron DeVore, we proposed a direct method (without specifically identifying any low dimensional set) that we called "sparse occupancy trees". The method defines a piecewise constant or linear approximation on general simplicial partitions. This talk considers an extension of this method to find a similar approximation on conforming simplicial partitions following an idea from a recent result together with Francesca Fierro and Andreas Veeser about nearbest approximation on conforming triangulations.

INI 1  
15:10 to 15:40  Afternoon Tea  
15:40 to 16:30 
Claire Boyer (Sorbonne Université); (ENS  Paris) Representer theorems and convex optimization
We establish a general principle which states that regularizing an inverse problem with a convex function yields solutions which are convex combinations of a small number of atoms. These atoms are identified with the extreme points and elements of the extreme rays of the regularizer level sets. As a side result, we characterize the minimizers of the total gradient variation. As an ongoing work, we will also study the geometry of the total gradient variation ball. This is a joint work with Antonin Chambolle, Yohann De Castro, Vincent Duval, Frédéric de Gournay, and Pierre Weiss. 
INI 1  
16:30 to 17:30  Welcome Wine Reception at INI 
09:00 to 09:50 
Simon Foucart (Texas A&M University) Functions of Few Coordinate Variables: Sampling Schemes and Recovery Algorithms
I will revisit in this talk the task of approximating
multivariate functions that depend on only a few of their variables. The number
of samples required to achieve this task to a given accuracy has been
determined for Lipschitz functions several years ago. However, two questions of
practical interest
remain: can we provide an explicit sampling strategy and
can we efficiently produce approximants? I will (attempt to) answer these
questions under some additional assumptions on the target function. Firstly, if
it is known to be linear, then the problem is exactly similar to the standard
compressive sensing problem, and I will review some of recent contributions
there. Secondly, if the target function is quadratic, then the problem connects
to sparse phaseless recovery and to jointly lowrank and bisparse recovery, for
which some results and open questions will be presented. Finally, if the target
function is known to increase coordinatewise, then the problem reduces to group
testing, from which I will draw the soughtafter sampling schemes and recovery
algorithms.

INI 1  
09:50 to 10:40 
Alexander Olevskii (Tel Aviv University) Discrete translates in function spaces
Given a Banach function space on R^n, does there exist
a uniformly discrete set of translates of a single function, which spans
the space? I'll present a survey on the problem and discuss recent
results, joint with A.Ulanovskii.

INI 1  
10:40 to 11:10  Morning Coffee  
11:10 to 12:00 
Olga Mula (Université ParisDauphine) Optimal algorithms for state estimation using reduced models 
INI 1  
12:00 to 13:30  Lunch at Churchill College  
13:30 to 14:20 
Joaquim OrtegaCerdà (Universitat de Barcelona) A sequence of wellconditioned polynomials We find an explicit sequence of polynomials of arbitrary degree with 
INI 1  
14:20 to 15:10 
Holger Rauhut (RWTH Aachen University) Linear and onebit compressive sensing with subsampled random convolutions
Compressive sensing predicts that sparse vectors can recovered from incomplete linear measurements with efficient algorithms in a stable way. While many theoretical results work with Gaussian random measurement matrices, practical applications usually demand for structure. The talk covers the particular case of structured random measurements defined via convolution with a random vector and subsampling (deterministic or random as well). We will give an overview on the corresponding theory and will cover also recent results concerning recovery from onebit measurements arising in quantized compressive sensing. Based on joint works with Felix Krahmer, Shahar Mendelson, Sjoerd Dirksen and HansChristian Jung. 
INI 1  
15:10 to 15:40  Afternoon Tea  
15:40 to 16:30 
Lukas Herrmann (ETH Zürich) QuasiMonte Carlo integration in uncertainty quantification of elliptic PDEs with logGaussian coefficients QuasiMonte Carlo (QMC) rules are suitable to overcome the curse of dimension in the numerical integration of highdimensional integrands. 
INI 1 
09:00 to 09:50 
Andreas Seeger (University of WisconsinMadison) Basis properties of the Haar system in various function spaces We present recent results on the Haar system in Besov and TriebelLizorkin spaces, with an emphasis on endpoint results. 
INI 1  
09:50 to 10:40 
Christoph Thiele (University of Bonn) On singular Brascamp Lieb integrals
We will give an overview over singular Brascamp Lieb integrals and discuss some recent results based on recent arxiv postings with Polona Durcik. 
INI 1  
10:40 to 11:10  Morning Coffee  
11:10 to 12:00 
Hans Feichtinger (University of Vienna); (University of Vienna) Approximation of continuous problems in Fourier Analysis by finite dimensional ones: The setting of the Banach Gelfand Triple
When it comes to the
constructive realization of operators arising in Fourier Analysis, be it the Fourier transform itself, or some convolution operator, or more generally an (underspread) pseudodiferential operator it is natural to make use of sampled version of the ingredients. The theory around the Banach Gelfand Triple (S0,L2,SO') which is based on methods from Gabor and timefrequency analysis, combined with the relevant function spaces (Wiener amalgams and modulation spaces) allows to provide what we consider the appropriate setting and possibly the starting point for qualitative as well as later on more quantitative error estimates. 
INI 1  
12:00 to 13:30  Lunch at Churchill College  
13:30 to 18:00  Free afternoon  
19:30 to 22:00 
Formal Dinner at Christ's College LOCATION Christ's College St Andrew's St, Cambridge CB2 3BU DRESS CODE Smart casual MENU Starter Camembert Fritters with Grape & Pecan Nut Salad Main course Braised Lamb Shank with Vegetables Open Ravioli with Squash & Porcini Mushrooms (V) Dessert Cambridge Burnt Cream with Strawberries or Raspberries 
09:00 to 09:50 
Sinan Güntürk (Courant Institute of Mathematical Sciences); (New York University) Extracting bits from analog samples: in pursuit of optimality
Sampling theorems, linear or nonlinear, provide the
basis for obtaining digital representations of analog signals, but often it is
not obvious how to quantize these samples in order to achieve the best
ratedistortion trade off possible, especially in the presence of redundancy.
We will present a general approach called
"distributed beta encoding" which can achieve superior (and often
nearoptimal) ratedistortion performance in a wide variety of sampling
scenarios. These will include some classical problems in the linear setting
such as Fourier and Gabor sampling, and some others in the nonlinear setting,
such as compressive sampling, spectral superresolution, and phase retrieval.

INI 1  
09:50 to 10:40 
Peter Richtarik (University of Edinburgh); (King Abdullah University of Science and Technology (KAUST)); (Moscow Institute of Physics and Technology) Stochastic QuasiGradient Methods: Variance Reduction via Jacobian Sketching
We develop a new family of variance reduced stochastic gradient descent methods for minimizing the average of a very large number of smooth functions. Our method—JacSketch—is motivated by novel de velopments in randomized numerical linear algebra, and operates by maintaining a stochastic estimate of a Jacobian matrix composed of the gradients of individual functions. In each iteration, JacSketch efficiently updates the Jacobian matrix by first obtaining a random linear measurement of the true Jacobian through (cheap) sketching, and then projecting the previous estimate onto the solution space of a linear matrix equa tion whose solutions are consistent with the measurement. The Jacobian estimate is then used to compute a variancereduced unbiased estimator of the gradient, followed by a stochastic gradient descent step. Our strategy is analogous to the way quasiNewton methods maintain an estimate of the Hessian, and hence our method can be seen as a stochastic q uasigradient method. Indeed, quasiNewton methods project the current Hessian estimate onto a solution space of a linear equation consistent with a certain linear (but nonrandom) measurement of the true Hessian. Our method can also be seen as stochastic gradient descent applied to a controlled stochastic optimization reformulation of the original problem, where the control comes from the Jacobian estimates. We prove that for smooth and strongly convex functions, JacSketch converges linearly with a meaningful rate dictated by a single convergence theorem which applies to general sketches. We also provide a refined convergence theorem which applies to a smaller class of sketches, featuring a novel proof technique based on a stochastic Lyapunov function. This enables us to obtain sharper complexity results for variants of JacSketch with importance sampling. By specializing our general approach to specific sketching strategies, JacSketch reduces to the celebrated stochastic average gradient (SAGA) method, and Coauthors: Robert Mansel Gower (Telecom ParisTech), Francis Bach (INRIA  ENS  PSL Research University) 
INI 1  
10:40 to 11:10  Morning Coffee  
11:10 to 12:00 
Boris Kashin (Steklov Mathematical Institute, Russian Academy of Sciences); (Russian Academy of Sciences) On some theorems on the restriction of operator to coordinate subspace 
INI 1  
12:00 to 13:30  Lunch at Churchill College  
13:30 to 14:20 
Yuan Xu (University of Oregon) Orthogonal structure in and on quadratic surfaces
Orthogonal structure in and on quadratic
surfaces Text of abstract: Spherical harmonics are orthogonal polynomials on
the unit sphere. They are eigenfunctions of the LaplaceBeltrami operator on
the sphere and they satisfy an addition formula (a closed formula for their
reproducing kernel). In this talk, we consider orthogonal polynomials on
quadratic surfaces of revolution and inside the domain bounded by quadratic
surfaces. We will define orthogonal
polynomials on the surface of a cone that possess both characteristics of
spherical harmonics. In particular, the addition formula on the cone has a
onedimensional structure, which leads to a convolution structure on the cone
useful for studying Fourier orthogonal series. Furthermore, the same narrative
holds for orthogonal polynomials defined on the solid cones.

INI 1  
14:20 to 15:10 
Gitta Kutyniok (Technische Universität Berlin) Beating the Curse of Dimensionality: A Theoretical Analysis of Deep Neural Networks and Parametric PDEs
Highdimensional parametric partial differential equations (PDEs) appear in various contexts including control and optimization problems, inverse problems, risk assessment, and uncertainty quantification. In most such scenarios the set of all admissible solutions associated with the parameter space is inherently low dimensional. This fact forms the foundation for the socalled reduced basis method. Recently, numerical experiments demonstrated the remarkable efficiency of using deep neural networks to solve parametric problems. In this talk, we will present a theoretical justification for this class of approaches. More precisely, we will derive upper bounds on the complexity of ReLU neural networks approximating the solution maps of parametric PDEs. In fact, without any knowledge of its concrete shape, we use the inherent lowdimensionality of the solution manifold to obtain approximation rates which are significantly superior to those provided by classical approximation results. We use this lowdimensionality to guarantee the existence of a reduced basis. Then, for a large variety of parametric PDEs, we construct neural networks that yield approximations of the parametric maps not suffering from a curse of dimensionality and essentially only depending on the size of the reduced basis. This is joint work with Philipp Petersen (Oxford), Mones Raslan, and Reinhold Schneider. 
INI 1  
15:10 to 16:10  Afternoon tea and poster session 
09:00 to 09:50 
Aicke Hinrichs (Johannes Kepler Universität) How good is random information compared to optimal information?
We study approximation and integration problems and compare the quality of optimal information with the quality of random information. For some problems random information is almost optimal and for other problems random information is much worse than optimal information. We give a survey about known and new results. Parts of the talk are based on joint work with D. Krieg, E. Novak, J. Prochno and M. Ullrich.

INI 1  
09:50 to 10:40 
Karlheinz Groechenig (University of Vienna) Totally positive functions in sampling theory and timefrequency analysis
Totally positive functions play an important role in
approximation theory and statistics. In this talk I will present recent new
applications of totally positive functions (TPFs) in sampling theory and
timefrequency analysis.
(i) We study the sampling problem for shiftinvariant
spaces generated by a TPF. These spaces arise the span of the integer shifts of
a TPF and are often used as a
substitute for bandlimited functions. We give a complete
characterization of sampling sets
for a shiftinvariant space with a TPF generator of
Gaussian type in the style of Beurling.
(ii) A related problem is the question of Gabor frames,
i.e., the spanning properties of timefrequency shifts of a given function. It
is conjectured that the lattice shifts of a TPF generate a frame, if and only
if the density of the lattice exceeds 1.
At this time this conjecture has been proved
for two important subclasses of TPFs. For rational lattices it is true for arbitrary
TPFs. So far, TPFs seem to be the only
window functions for which the fine structure of the associated Gabor frames is tractable.
(iii) Yet another question in timefrequency analysis is
the existence of zeros of the Wigner distribution (or the radar ambiguity
function). So far all examples of zerofree ambiguity functions are related to
TPFs, e.g., the ambiguity function of the Gaussian is zero free.

INI 1  
10:40 to 11:10  Morning Coffee  
11:10 to 12:00 
Feng Dai (University of Alberta) Integral norm discretization and related problems
In this talk, we will discuss the problem of replacing an integral norm with respect to a given probability measure by the corresponding integral norm with respect to a discrete measure. We study the problem for elements of finite dimensional spaces in a general setting, paying a special attention to the case of the multivariate trigonometric polynomials with frequencies from a finite set with fixed cardinality. Both new results and a survey of known results will be presented. This is a joint work with A. Prymak, V.N. Temlyakov and S. Tikhonov. 
INI 1  
12:00 to 13:30  Lunch at Churchill College  
13:30 to 14:20 
Milana Gataric (University of Cambridge) Imaging through optical fibres
In this talk, I'll present some recent results on reconstruction of optical vectorfields using measurements acquired by an optical fibre characterised by a nonunitary integral transform with an unknown spatiallyvariant kernel. A new imaging framework will be introduced, which through regularisation is able to recover an optical vectorfield with respect to an arbitrary representation system potentially different from the one used for fibre calibration. In particular, this enables the recovery of an optical vectorfield with respect to a Fourier basis, which is shown to yield indicative features of increased scattering associated with tissue abnormalities. The effectiveness of this framework is demonstrated using biological tissue samples in an experimental setting where measurements are acquired by a fibre endoscope, and it is observed that indeed the recovered Fourier coefficients are useful in distinguishing healthy tissues from lesions in early stages of cancer. If time permits, I'll also briefly present a new method that enables recovery of nonunitary fibre transmission matrices necessary for minimally invasive optical imaging in inaccessible areas of the body.

INI 1  
14:20 to 15:10 
Geno Nikolov (Sofia University St. Kliment Ohridski) Markovtype inequalities and extreme zeros of orthogonal polynomials
The talk is centered around the problem of finding
(obtaining tight twosided bounds
for) the sharp constants in certain
MarkovBernstein type inequalities in weighted $L_2$ norms. It turns out that,
under certain assumptions, this problem is equivalent to the estimation of the
extreme zeros of orthogonal polynomials with respect to a measure supported on
$R_{+}$.
It will be shown how classical tools like the
EulerRayleigh method and Gershgorin circle theorem produce surprisingly good
bounds for the extreme zeros of the Jacobi, Gegenbauer and Laguerre
polynomials. The sharp constants in the $L_2$
Markov inequalities with the Laguerre and Gegenbauer weight functions
and in a discrete $\ell_2$ MarkovBernstein inequality are investigated using
the same tool.

INI 1 