Minisymposia

Speaker: Antonio Salmerón (U. of Almería, Spain)
Title: High dimensional hybrid Bayesian networks: Is there life beyond the exponential family?
Abstract: Within the context of hybrid Bayesian networks, the problem of high dimensionality is challenging both from the point of view of parameter estimation and probabilistic inference. While parameter estimation can be efficiently carried out, specially for models within the exponential family of distributions, it sometimes comes along with limitations on the network structure or costly probabilistic inference/Bayesian updating schemes. On the other hand, probabilistic models based on mixtures of truncated basis functions (MoTBFs) have turned out to be compatible with efficient probabilistic inference schemes. However, MoTBFs do not belong to the exponential family, which makes the parameter estimation process more problematic due to, for instance, the non-existence of fixed dimension sufficient statistics (but the sample itself). In this work we explore some reparameterizations of MoTBFs distributions that make possible the use of efficient likelihood-based parameter
estimation procedures.

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Speaker: Nikolas Bernaola (Technical University of Madrid, Spain)
Title: Learning and visualizing massive Bayesian networks with FGES-Merge and BayeSuites
Abstract: In this work we present a new algorithm, FGES-Merge, for learning massive Bayesian networks of the order of tens of thousands of nodes by using properties of the topology of the network and improving the parallelization of the arc search procedure. We use the algorithm to learn a network for the full human genome using expression data from the brain and to aid with the interpretation of the results, we present the BayeSuites web tool, which allows for the visualization of the network and gives a GUI for inference and search over the network avoiding the typical scalability problems of networks of this size.

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Speaker: Ofelia Paula Retamero Pascual (U. of Granada, Spain)
Title: Approximation in Value-Based Potentials
Abstract: When dealing with complex models (i.e., models with many variables, a high degree of dependency between variables, or many states per variable), the efficient representation of quantitative information in probabilistic graphical models (PGMs) is a challenging task. To address this problem, Value-Based Potentials (VBPs) leverage repeated values to reduce memory requirements when managing Bayesian Networks or Influence Diagrams. In this work, we propose how to approximate VBPs to achieve a greater reduction in the memory space required and thus be able to deal with more complex models.

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Speaker: Borja Sánchez-López (IIIA-CSIC, Spain)
Title: Convergent and fast natural gradient based optimization method DSNGD and adaptation to large dimensional Bayesian networks
Abstract: Information geometry has shown that probabilistic models are twisted and distorted manifolds compared to standard Euclidean spaces. In such cases where every point of the manifold describes a probability distribution, Fisher information metric (FIM) becomes handy to correctly observe the space and their local measure as it actually is. For example, the gradient of a function defined on such manifold is not even well defined until we apply metric information to it. Once FIM is considered, the steepest ascent direction is available and well defined, this is the so-called natural gradient.

Dual stochastic natural gradient descent (DSNGD) is our version of a natural gradient based algorithm to optimize the conditional log-likelihood of a class variable Y given features X. It is convergent and its computational complexity is linear, when X is discrete. We define DSNGD and take a glance to its convergence property. Some experiments are discussed paying special attention to the performance enhancement acquired after convergence property, with respect to standard non convergent stochastic natural gradient descent (SNGD). We extend DSNGD to Bayesian networks where the log-odds ratio of P(Y|X) is an affine function of features. Since DSNGD is showing low computational complexity, it scales nicely as dimension of the manifold grows.

Speaker: Stefano Castruccio (University of Notre Dame, USA)
Title: Calibration of Spatial Forecasts from Citizen Science Urban Air Pollution Data with Sparse Recurrent Neural Networks
Abstract: T.b.a.

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Speaker: Aritz Adin (Universidad Publica de Navarra)
Title: Scalable Bayesian models for spatio-temporal count data
Abstract: T.b.a.

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Speaker: Ying Sun (KAUST University)
Title: DeepKriging: Spatially Dependent Deep Neural Networks for Spatial Prediction
Abstract: T.b.a.

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Speaker: Marc Genton (KAUST University)
Title: Large-Scale Spatial Data Science with ExaGeoStat
Abstract: T.b.a.

Speaker: Amparo Baíllo
Title: Ensemble distance-based regression and classification for large sets of mixed-type data
Abstract: The distance-based linear model (DB-LM) extends the classical linear regression to the framework of mixed-type predictors or when the only available information is a distance matrix between regressors (as it sometimes happens with big data). The main drawback of these DB methods is their computational cost, particularly due to the eigendecomposition of the Gram matrix. In this context, ensemble regression techniques provide a useful alternative to fitting the model to the whole sample. This work analyzes the performance of three subsampling and aggregation techniques in DB regression on two specific large, real datasets. We also analyze, via simulations, the performance of bagging and DB logistic regression in the classification problem with mixed-type features and large sample sizes.

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Speaker: Anabel Forte Deltell
Title: Bayesian methods for variable selection. Challenges of the XXI Century.
Abstract: Model selection and, in particular Variable selection is, without doubt, one of the most difficult procedures in science. Along history it has been approached from different points of view as well as from different paradigms such as Frequentist or Bayesian statistics. Specifically in this talk we will review how Bayesian Statistics can deal with variable selection, trying to understand the advantages of this paradigm. Also we will try to point to the new challenges that the Era of high dimensional data adds to this already difficult task and how Bayes may deal with it.

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Speaker: Álvaro Méndez Civieta
Co-authors: M. Carmen Aguilera-Morillo; Rosa E. Lillo
Title: fPQR: A quantile based dimension reduction technique for regression.
Abstract: Partial least squares (PLS) is a well known dimensionality reduction technique used as an alternative to ordinary least squares (OLS) in collinear or high dimensional scenarios. Being based on OLS estimators, PLS is sensitive to the presence of outliers or heavy tailed distributions. Opposed to this, quantile regression (QR) is a technique that provides estimates of the conditional quantiles of a response variable as a function of the covariates. The usage of the quantiles makes the estimates more robust against the presence of heteroscedasticity or outliers than OLS estimators. In this work, we introduce the fast partial quantile regression algorithm (fPQR), a quantile based technique that shares the main advantages of PLS: it is a dimension reduction technique that obtains uncorrelated scores maximizing the quantile covariance between predictors and responses. But additionally, it is also a robust, quantile linked methodology suitable for dealing with outliers, heteroscedastic or heavy tailed datasets. The median estimator of the PQR algorithm is a robust alternative to PLS, while other quantile levels can provide additional information on the tails of the responses.

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Speaker: Pepa Ramírez-Cobo
Co-authors: Rafael Blanquero, Emilio Carrizosa, M. Remedios Sillero-Denamiel
Title: Variable selection for Naïve Bayes classification
Abstract: The Naïve Bayes has proven to be a tractable and efficient method for classification in multivariate analysis. However, features are usually correlated, a fact that violates the Naïve Bayes’ assumption of conditional independence, and may deteriorate the method’s performance. Moreover, datasets are often characterized by a large number of features, which may complicate the interpretation of the results as well as slow down the method’s execution.

In this paper we propose a sparse version of the Naïve Bayes classifier that is characterized by three properties. First, the sparsity is achieved taking into account the correlation structure of the covariates. Second, different performance measures can be used to guide the selection of features. Third, performance constraints on groups of higher interest can be included.

Speaker: Adrián Pérez-Suay, Universitat de València
Title: T.b.a.
Abstract: T.b.a.

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Speaker: Paula Gordaliza
Title: T.b.a.
Abstract: T.b.a.

 

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Speaker: : Jaume Abella and Francisco J. Cazorla (Barcelona Supercomputing Center)
Title: Certification Aspects in Future AI-Based High-Integrity Systems
Abstract: The trend towards increased autonomy functions in high-integrity systems, like those in planes and cars, causes disruptive changes to the certification process. At software level, the challenge relates to the increasing use of Artificial Intelligence (AI) based software to provide the levels of accuracy required. At the hardware level, it relates to the use of high-performance heterogeneous multi-core processors to provide the required level of computing performance and the impact multi-cores have on functional safety including software timing aspects. In this talk we will cover some of the main challenges brought by both, AI software and multi-cores, to the certification process of high-integrity systems. We will also discuss potential research paths to address those challenges.

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Speaker: Hristo Inouzhe
Title: T.b.a.
Abstract: T.b.a.

Speaker: Marc Hallin, ECARES and Department of Mathematics, Université libre de Bruxelles
Title: From Multivariate Quantiles to Copulas and Statistical Depth, and Back
Abstract: The univariate concept of quantile function–the inverse of a distribution function– plays a fundamental role in Probability and Statistics. In dimension two and higher, however, inverting
traditional distribution functions does not lead to any satisfactory notion. In their quest for the Grail of an adequate definition, statisticians dug out two extremely fruitful theoretical pathways: copula transforms, where marginal quantiles are privileged over global ones, and depth functions, where a center-outward ordering is substituting the more traditional South-West/North-East one. We show > how a recent center-outward redefinition, based on measure transportation ideas, of the concept of distribution function reconciles and fine-tunes these two approaches, and eventually yields a notion of multivariate quantile matching, in arbitrary dimension d, all the properties that make univariate quantiles a successful and vital tool of statistical inference.

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Speaker: José Antonio Carrillo de la Plata, Mathematical Institute, University of Oxford
Title: Consensus-Based Interacting Particle Systems and Mean-field PDEs for Optimization and Sampling
Abstract: We will start by doing a quick review on consensus models for swarming. Stability of patterns in these models will be briefly discussed. Then we provide an analytical framework for investigating the efficiency of a consensus-based model for tackling global optimization problems. We justify the optimization algorithm in the mean-field sense showing the convergence to the global minimizer for a large class of functions. An efficient algorithm for large dimensional problems is introduced. Theoretical results on consensus estimates will be illustrated by numerical simulations.

We then develop these ideas to propose a novel method for sampling and also optimization tasks based on a stochastic interacting particle system. We explain how this method can be used for the following two goals: (i) generating approximate samples from a given target distribution, and (ii) optimizing a given objective function. This approach is derivative-free and affine invariant, and is therefore well-suited for solving complex inverse problems, allowing (i) to sample from the Bayesian posterior and (ii) to find the maximum a posteriori estimator. We investigate the properties of this family of methods in terms of various parameter choices, both analytically and by means of numerical simulations.

This talk is a summary of works in collaboration with Y.-P. Choi, O. Tse, C. Totzeck, F. Hoffmann, A. Stuart and U. Vaes.

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Speaker: Alberto González Sanz, Institut de Mathématiques de Toulouse and ANITI
Title: Central Limit Theorems for General Transportation Costs
Abstract: One of the main ways to quantify the distance between distributions is the well known Wasserstein metric. In Statistics and Machine Learning applications it is increasingly common to deal with measures supported on a high dimensional space. Some recents results show that the Wasserstein metric suffers from the curse of dimensionality, which means that its empirical approximation becomes worse as dimension grows. We will explain a new method based on the Efron-Stein inequality and on the sequential compactness of the closed unit ball in $L^2 (P)$ for the weak topology that improves a result of del Barrio and Loubes (2019) and states that, even if the empirical Wasserstein metric converges with slow rate, its oscillations around its mean are asymptotically Gaussian with rate $\sqrt{n}$, $n$ being the sample size, which means that the curse of dimensionality is avoided in such a case. Finally, we will present some applications of these results to statistical and data science problems.

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Speaker: Jean-Michel Loubes, Institut de Mathématiques de Toulouse and ANITI
Title: T.b.a.
Abstract: T.b.a.

Speaker: D. Ríos Insua, R. Naveiro (ICMAT), J. Poulos (Harvard)
Title: Adversarial Machine Learning. An overview
Abstract: Adversarial machine learning aims at robustifying machine learning algorithms against possible actions from adversaries. Most earlier work in AML has modelled the confrontation between learning systems and adversaries as a 2-agent game from a game theoretic perspective. After briefly overviewing previous work, we shall present an alternative framework based on adversarial risk analysis.

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Speaker: F. Ruggeri (CNR-IMATI), V. Gallego, A. Redondo (ICMAT)
Title: Bayesian approaches to protecting classifiers from attacks
Abstract: A major area within adversarial machine learning deals with producing classifiers that are robust to adversarial data manipulations. This talk will present formal Bayesian approaches to this problem considering settings in which robustification takes place at training time and at operation time.

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Speaker: R. Naveiro (ICMAT), T. Ekin (Texas State), A. Torres (ICMAT)
Title: Augmented probability simulation for optimization in adversarial machine learning
Abstract: Adversarial machine learning from an adversarial risk analysis perspective entails a cumbersome computational procedure in which one first simulates from the attacker problem to forecast attacks and then includes such forecasts in the Defender problem to be optimized. We shall present how the procedure may be streamlined with the aid of augmented probability simulation approaches.

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Speaker: D. García-Rasines, C. Guevara, S. Rodríguez-Santana (ICMAT)
Title: Adversarial machine learning for financial applications
Abstract: Numerous business applications entail dynamic competitive decision environments under uncertainty. We shall sketch how adversarial machine learning methods may be used in such domains, illustrating the ideas with problems in relation to pension funds, loans and the stock market.

Speaker: Santiago Mazuelas, Basque Center for Applied Mathematics (BCAM), Bilbao, Spain
Title: Minimax Classification with 0-1 Loss and Performance Guarantees
Abstract: Supervised classification techniques use training samples to find classification rules with small expected 0-1 loss. Conventional methods achieve efficient learning and out-of-sample generalization by minimizing surrogate losses over specific families of rules. This talk presents minimax risk classifiers (MRCs) that do not rely on a choice of surrogate loss and family of rules. MRCs achieve efficient learning and out-of-sample generalization by minimizing worst-case expected 0-1 loss w.r.t. uncertainty sets that are defined by linear constraints and include the true underlying distribution. In addition, MRCs’ learning stage provides performance guarantees as lower and upper tight bounds for expected 0-1 loss. We also present MRCs’ finite-sample generalization bounds in terms of training size and smallest minimax risk, and show their competitive classification performance w.r.t. state-of-the-art techniques using benchmark datasets.

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Speaker: Rafael Cabañas, Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), Lugano, Switzerland
Title: What if causal models were imprecise?
Abstract: Causality is currently an emerging direction for data science with a wealth of potential applications in diverse domains such as Artificial Intelligence, Economics, Social Science or Medicine. Pearl’s structural causal models are a natural formalism for causal inference, in particular for their appealing graphical representation. However, the peculiar features of causal models may render them not always easy to access to a traditional audience, which is instead familiar with pre-existing graphical tools and related procedures. Structural causal models can be then transformed into equivalent credal networks. This means that every query on the causal model can be reformulated as a query on the imprecise model, which can then be solved by standard algorithms for the latter. Moreover, this also allows producing bounds for unidentifiable queries.

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Speaker: Ekhine Irurozki,LTCI, Telecom Paris, Institut Polytechnique de Paris, Paris, France
Title: Concentric Mixtures of Mallows Models for Top-$k$ Rankings
Abstract: Mixtures of two Mallows models for top-k rankings with equal location parameters but with different scale parameters arise when we have a heterogeneous population of voters formed by two populations, one of which is a subpopulation of expert voters. They are denoted as concentric mixtures of Mallows models.
We show the identifiability of both components and the learnability of their respective parameters. These results are based upon, first, bounding the sample complexity for the Borda algorithm with top-k rankings. Second, we characterize the distances between rankings, showing that an off-the-shelf clustering algorithm separates the rankings by components with high probability -provided the scales are well-separated. As a by-product, we include an efficient sampling algorithm for Mallows top-k rankings. Finally, since the rank aggregation will suffer from a large amount of noise introduced by the non-expert voters, we adapt the Borda algorithm to be able to recover the ground truth consensus ranking which is especially consistent with the expert rankings.

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Speaker: Aritz Perez, Basque Center for Applied Mathematics (BCAM), Bilbao, Spain
Title: Learning decomposable models by coarsening
Abstract: During the last decade, some exact algorithms have been proposed for learning decomposable models by maximizing additively decomposable score functions, such as Log-likelihood, BDeu, and BIC. However, up to the date, the proposed exact approaches are practical for learning models up to $20$ variables. In this work, we present an approximated procedure that can learn decomposable models over hundreds of variables with a remarkable trade-off between the quality of the obtained solution and the quantity of computational resources required. The proposed learning procedure iteratively constructs a sequence of coarser decomposable (chordal) graphs. At each step, given a decomposable graph, the algorithm adds the subset of edges due to the actual minimal separators that maximize the score function while maintaining the chordality. The proposed procedure has shown competitive results for learning decomposable models over hundred of variables using a reasonable amount of computational resources. Finally, we empirically show that it can be used to reduce the search space of exact procedures, which would allow them to address the learning of high-dimensional decomposable models.

Speaker: Anne Elorza
Title: Taxonomization of Combinatorial Optimization Problems in Fourier Space
Abstract: In the field of permutation-based Combinatorial Optimization Problems, those classified as NP-hard represent a major challenge, since the cost of exact algorithms becomes prohibitive. As an alternative, metaheuristic algorithms have been proposed. However, there still exists a major difficulty in their application: given a specific problem instance, and considering the great variety of possible algorithms, how could we select the most appropriate algorithm for solving it? A first step to try to solve this problem would be to create a taxonomy that groups together problem instances that can be solved efficiently by the same algorithms. In this talk, we explain the theoretical framework that we have adopted in order to construct such a taxonomy, by making use of the Fourier characteristics of each problem instance. As with the classical Fourier transform over the real line, which decomposes a function into a sum of sines and cosines, the Fourier transform over the symmetric group decomposes a permutation-based function into a linear combination of basis functions. Therefore, an objective function can be described through its Fourier coefficients, and we plan to use this information to create the taxonomy.

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Speaker: Jose A. Lozano
Title: Construct, merge, solve & adapt: a new general algorithm for combinatorial optimization
Abstract: In this talk we present Construct, Merge, Solve & Adapt (CSMA): A Recent Hybrid Approach for Combinatorial Optimization. This algorithm provides a means for taking profit from exact techniques (such as, for example, general-purpose integer linear programming (ILP) solvers) in the context of problem instances that are much too large for solving them with the exact technique directly. In this presentation, we introduce the algorithm and show its successful application in the solution of several combinatorial optimization problems.

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Speaker: David Romero
Title: Optimize your path
Abstract: Urban mobility has become through the last decades as one of the key pillars of sustainability and energy efficiency as urban population increases and by consequences the number of private vehicles does it too. However, the problem of offering a public transport that is sustainable, optimizes the maximum resources available and, at the same time, offers the best possible service to users is a complicated problem. One possible solution is to consider a flexible transport service strategy is considered, based on the idea behind ride-sharing services. This model is based on the concept of a tailored ride with a starting and an ending point previously agreed by the user and the bus company with the expected departure, arrival and fare but shared with other users who would have the same experience, making the journey faster, more comfortable and sustainable.
The aim of this talk is to explain, by using a concrete situation, how one can deal with that routing problem by using optimization algorithms tailored for our needs.

Speaker: Helena Ramalhinho
Title: Optimization for Social Good
Abstract: Analytics focuses on transforming data into insights by applying advanced analytical method, based on mathematics, statistics, operations research and artificial intelligent models and algorithms, with the objective to improve the performance of an organization. One of the main tools in Analytics is Optimization. In this talk, we present the optimization tools and methodologies applied to NonProfit Organizations (NPO). We will describe applications of Mathematical Programming Models and Metaheuristics Algorithms to Social Care, Healthcare, Humanitarian Logistics and Environmental organizations. Examples of applications of Optimization in these organizations are: home health care logistics and scheduling; planning disaster response and preparedness to improved decision-making; location of the primary health care centers or schools; planning the humanitarian aid distribution; planning a sustainable transportation; location of electrical charge stations, etc. We will discuss also the main aspects of these models and algorithms, and the main differences to other more frequent applications, as in manufacturing and retailing industries.

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Speaker: Jordi Castro
Title: A new interior-point optimization approach for support vector machines for binary classification and outlier detection
Abstract: In this work we present a new interior-point optimization method for the solution of 2-class and 1-class linear support vector machines (SVMs), which are, respectively, used for binary classification and outlier detection. Unlike previous interior-point approaches for SVMs, which were only practical when the dimension of the points was small, the new proposal can also deal with high-dimensional data.
The new approach is compared with state-of-the-art solvers for SVMs, either based on interior-point algorithms (such as SVM-OOPS), or specific algorithms developed by the machine learning community (such as LIBSVM and LIBLINEAR).

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Speaker: Asunción Jiménez-Cordero
Coauthors: Juan Miguel Morales and Salvador Pineda
Title: An offline-online strategy to improve MILP performance via Machine Learning tools.
Abstract: Solving large-scale Mixed Integer Linear Problems (MILP) is well known to be a challenging task. To alleviate their computational burden, several works in the literature have proposed Machine Learning techniques to identify and remove constraints. However, all these techniques report that a non-negligible percentage of the obtained solutions are infeasible since they violate some of the removed constraints.
This talk presents an offline-online strategy that improves the quality of the available data to significantly reduce the number of infeasible solutions. By linking Mathematical Optimization and Machine Learning, our approach leads to substantial performance improvements in terms of feasibility and computational time, which we demonstrate through synthetic and real-life MILP problems.

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Speaker: José Niño Mora
Title: Data-driven dynamic priority allocation: recent advances
Abstract: This talk will present recent advances on data-driven dynamic priority allocation models based on the restless bandit framework and on dynamic priority indices. The focus is on partially observed Markov decision models with a Bayesian data-incorporation mechanism, motivated by diverse application areas. The results include approaches for establishing existence of the indices and for computing them efficiently. Evidence will be presented of the practical value of the proposed approach.

Speaker: Begoña Vitoriano, Adán Rodríguez-Martínez, M. Teresa Ortuño
Title: Strategic and tactical preparedness in humanitarian logistics based on scenario generation from historical data
Abstract: T.B.A.

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Speaker: M. Teresa Ortuño, Inmaculada Flores, Gregorio Tirado
Title: Evacuation and supply distribution facing a natural disaster
Abstract: T.B.A.

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Speaker: Bibiana Granda, Javier León, Begoña Vitoriano, John Hearne
Title: Optimisation models for wildfire suppression
Abstract: T.B.A.

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Speaker: Adán Rodríguez-Martínez, Begoña Vitoriano, Gonzalo Barderas
Title: Wildfire risk measurement for fuel management decision-making using stochastic scenarios and Bayesian networks
Abstract: T.B.A.

Speaker: Isabel Rodrigo Rincón. Dirección asistencial del Complejo Hospitalario de Navarra
Title: Problems and challenges of the health management
Abstract: T.B.A.

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Speaker: Marta Cildoz Esquíroz, Fermín Mallor. Research group q-UPHS.
Title: Using Electronic Health Record for the management of the patient flow in the Hospital Emergency Department.
Abstract: T.B.A.

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Speaker: Daniel García de Vicuña, Laida Esparza, Fermín Mallor. Research group q-UPHS.
Title: Analysis of decision-making data for understanding and helping the ICU management.
Abstract: T.B.A.

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Speaker: Laura Frías, Marta Cildoz, Daniel García de Vicuña, Martín Gastón, Cristina Azcárate, Fermín Mallor. Research group q-UPHS.
Title: Deployment and control of rural emergencies resources.
Abstract: T.B.A.

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Speaker: Fermín Mallor, Marta Cildoz, Pedro Mateo. Research group q-UPHS.
Title: A balanced planning of ICU physician shifts for heterogeneous staff.
Abstract: T.B.A.

Speaker: Sandra Benítez-Peña
Coauthors: Rafael Blanquero, Emilio Carrizosa, Pepa Ramírez-Cobo
Title: Linear regression analysis on probabilistic-linked data
Abstract: Data linkage is a task used for merging data sets that contain information of the same entities, but lack of unique identification codes. Real datasets come from an exact matching, however, the procedure of data merging does not need to be exact: a single entity can be linked to two or more instances if they are similar enough. In this talk, we present a novel Non-Linear Programming model that integrates, in a single formulation, the task of obtaining a probabilistic matching and that also performs linear regression using such obtained linked data. Numerical results are presented for both simulated and real data sets, demonstrating the power of our methodology. Also, heuristics for providing good initial solutions are presented here.

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Speaker: Nuria Gómez-Vargas
Coauthors: Rafael Blanquero Bravo, Elisa Isabel Caballero Ruiz, Emilio Carrizosa Priego, Marina Enguidanos Weyler, Ana Gema Galera Pozo, Jasone Ramírez-Ayerbe
Title: Machine Learning defines innovation
Abstract: The presence of companies on the internet has been fundamental for theirgrowth in recent years. For this reason, the exploitation of their webpages isproposed as a way of characterizing them. However, the vast magnitude ofthe variables that can be extracted from these sites makes their treatment aproblem.In this respect, we have developed a machine learning tool in order tocharacterize the innovation of a company. First, we have defined a preprocessingstep applying text mining techniques to the respective webpages, followed bydifferent dynamics of grouping and selecting words and html tags that bringout their relevance. Finally, we classify companies according to their innovationusing random forests. With this methodology, we obtain not only a distinctionbetween companies that are innovative or not, but also a definition of innovationaccording to the importance of the variables.

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Speaker: M Cristina Molero-Río
Title: T.B.A.
Abstract: T.B.A.

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Speaker: Jasone Ramírez-Ayerbe
Coauthors: Emilio Carrizosa, Dolores Romero Morales
Title: Counterfactual Explanations via Mathematical Optimization
Abstract: Due to the increasing use of complex machine learning models, often seen as “black boxes”, it has become more and more important to be able to understand and explain their behaviour, and thus ensure transparency and fairness. An effective class of post-hoc explanations are counterfactual explanations, i.e. minimal perturbations of the predictor variables to change the prediction for a specific instance. We propose a multi-objective mathematical formulation for different state-of-the-art models based on scores, including tree ensemble classifiers and linear models. We formulate the problem at individual and group level. Real-world data has been used to illustrate our method.

Speaker: Vanesa Guerrero
Title: On some mathematical optimization models to gain insight into complex data
Abstract: Mathematical Optimization plays a crucial role to extract knowledge from data and cope with nowadays requirements in decision making processes. The increase in data complexity has made, in some cases, the classical statistical tools obsolete and more sophisticated frameworks are thus needed. In particular, dimensionality reduction techniques demand an update to face the new challenges posed by different data structures and to make the new features interpretable. In this talk, we review some mathematical optimization approaches which have helped to enhance the interpretability of the low-dimensional embeddings produced by different dimensionality reduction techniques and in different contexts.

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Speaker: Manuel Navarro-García
Title: On a semidefinite optimization approach to estimate smooth hypersurfaces using P-splines and shape constraints
Abstract: In this talk, we address the problem of estimating smooth hypersurfaces in a regression problem for data lying on large grids, and where the fit of the data has to satisfy shape constraints such as non-negativity or monotonicity in a certain direction. We assume that the smooth hypersurface to be estimated is defined through a tensor product of reduced-rank basis (B−splines) and fitted by means of P-splines. In order to incorporate these requirements, a semidefinite programming approach is developed which, for the first time, successfully conveys out-of-range constrained forecasting. The usefulness of our methodology is illustrated in simulated and real data related to demography as well as data arising in the context of the COVID-19 pandemic.

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Speaker: Víctor Blanco
Coauthors: Alberto Japón, Justo Puerto
Title: Mathematical Optimization approaches to supervised learning with noisy labels
Abstract: The primary goal of supervised classification is to find patterns from a training sample of labeled data in order to predict the labels of out-of-sample data, in case the possible number of labels is finite. Among the most relevant applications of classification methods are those related with security, as in spam filtering or intrusion detection. The main difference of these applications with respect to other uses of classification approaches is that malicious adversaries can adaptively manipulate their data to mislead the outcome of an automatic analysis. In this work we propose novel methodologies to optimally construct classifiers that take into account that label noises occur in the training sample. We propose different alternatives based on solving Mixed Integer Linear and Non Linear models by incorporating decisions on relabeling some of the observations in the training dataset. This feature is adequatelly embedded into different types of optimization-based classifiers, as SVM or Decision Trees. Extensive computational experiments are reported based on a battery of standard datasets taken from UCI Machine Learning repository, showing the effectiveness of the proposed approaches.

Speaker: Víctor Aceña Gil
Title: Client scoring for a tourism agency based on Machine Learning and Utility Theory
Abstract: A travel agency’s main resource is its agents. They provide quotes to all clients who request them, whether the purchase is consolidated or not. However, depending on the purpose of the trip, this can be time-consuming for the agent and waste time and money for the agency. Typically, a manager is responsible for allocating each budget to one agent or another, based on his or her expert judgement and knowledge of each agent’s capabilities. This talk will present a scoring method that uses utility functions based on the expected net profit of each potential customer. The net profit per travel package is easily estimated, taking into account the cost per agent over all the days it takes to prepare each quote and the price of the travel package. In addition, a machine learning model estimates the probability of purchase for each customer needed to construct the utility function.

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Speaker: Isaac Martín de Diego
Title: Data Science success stories
Abstract: Data science is defined at the intersection of three broad areas: mathematics, computer science and an application domain. Typically, the academic environment provides a high degree of expertise in the first two, and a low degree of interaction with industry. In this talk we address some of the success stories that the data science laboratory of the King Juan Carlos University has achieved in domains as diverse as cattle breeding, health, the chemical energy sector, telecommunications, and tourism.

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Speaker: Oriol Ramos
Title: Graph-based approaches for document information extraction
Abstract: Document information extraction is a classic task in image processing and computer vision. From the first OCR systems, currently distributed within home scanners, to unconstrained handwriting recognition, the main challenge in this field is not the “simple” content transcription but to extract information to feed database systems. To this end, it is needed to understand the document content context. In this talk, we will briefly review the main techniques developed in this field and we will focus on two particular ones such as table detection and table understanding. For these two tasks we will explain some recent graph-based approaches that we have recently developed using latest advances in deep learning. We will also discuss some of the main difficulties when dealing with real data, like for instance, the lack of (annotated) data and some of the most successful strategies currently used to deal with.

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Speaker: Pau Fonseca
Title: Using a Digital Twin to forecast the SARS-CoV-2 spread in Catalonia
Abstract: We explore a Digital Twin approach to model the spread of SARS-CoV-2 in Catalonia. Our Digital Twin is composed of three different dynamics models. These three models are used to perform validation using the Model Comparison approach. In this talk, we will discuss the Digital Twin structure, and how we use the Validation process to obtain knowledge from the system. This allows us to understand the effects of the nonpharmaceutical interventions. To simplify the maintenance of the dynamic compartmental model for the SARS-CoV-2 spread forecast we use Specification and Description Language (SDL) to represent it. This simplifies the model assumptions understanding by the different specialists involved in the Digital Twin maintenance and use; assumptions that must be validated continuously following a Solution Validation approach. We will discuss the Digital Twin adoption in the decision-making process and the implications of the discussion based on models.

Speaker: Eugenio Hernández, Universidad Autónoma de Madrid
Title: Theoretical aspects of non-linear approximation.
Abstract: T.B.A.

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Speaker: Demetrio Labate, University of Houston
Title: Analysis of the image inpainting problem using sparse multiscale representations and CNNs.
Abstract: Image inpainting is an image processing task aimed at recovering missing blocks of data in an image or a video. In this talk, I will show that sparse multiscale representations offer both an efficient algorithmic framework and a well-justified theoretical setting to address the image inpainting problem. I will start by formulating inpainting in the continuous domain as a function interpolation problem in a Hilbert space, by adopting a formulation previously introduced by King et al. [2014]. As images found in many applications are dominated by edges, I will assume a simplified image model consisting of distributions supported on curvilinear singularities. I will prove that the theoretical performance of image inpainting depends on the microlocal properties of the representation system, namely exact image recovery is achieved if the size of the missing singularity is smaller than the size of the structure elements of the representation system. A consequence of this observation is that a shearlet-based image inpainting algorithm – exploiting their microlocal properties – significantly outperforms a similar approach based on more traditional multiscale methods. In the second part of the talk, I will apply this theoretical observation to improve a state-of-the-art algorithm for blind image inpainting based on Convolutional Neural Networks.

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Speaker: Gemma Huguet, Universidad Politécnica de Cataluña
Title: Neuronal models for visual perception in ambiguous visual scenes
Abstract: When observers view for an extended time an ambiguous visual scene (admitting two or more different interpretations), they report spontaneous switching between different perceptions. The most studied case is perceptual bistability (two interpretations), which includes binocular rivalry (alternation of two different images, one presented to each eye), but there are other cases in which ambiguous images may show phenomena of tristability and much more complex dynamics. Models of multistable perception include models with multiple attractors and with heteroclinic cycles. In both models, noise is added to account for the irregular oscillations. In this talk, we will discuss the main features of these models and we will show how they can account for the dynamical properties (transition probabilities, distributions of percept durations, etc) observed in the experiments. Finally, we discuss the role of noise and we show that in the heteroclinic network models it can be replaced by quasi-periodic perturbations, assuming that the system is receiving events, either internal or from other brain areas, that include only a finite number of (incongruent) frequencies.

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Speaker: Davide Barbieri, Universidad Autónoma de Madrid
Title: Abstract harmonic analysis and image reconstruction in primary visual cortex
Abstract: Human vision has inspired several advances in harmonic analysis, especially wavelet analysis, and it has been the main source of heuristics for the development of neural network architectures devoted to image processing. One of the most studied neural structures in brain’s visual cortex is area V1, where neurons perform a wavelet-like analysis that is generally considered to be associated with the group structure of rotations and translations. It is indeed possible to model part of the perceptual behavior of the network of neural cells in V1 as a projection of the image onto one, or more, orbits of that group, and consequently associate to each neuron in V1 a parameter of the group. However, due to the physical constraint of having a neural displacement onto a two dimensional layer, the group is not fully, nor uniformly, represented in V1. The represented subset of the group has however a characteristic geometric structure, that has been modeled over the physiological measurements of what are called orientation preference maps. A natural question posed by this empirical observation is whether the missing part of the group, and of the corresponding wavelet coefficients, has perceptual consequences, and if, on the other hand, it is possible to recover or estimate in some stable way the missing information. The ability to perform such a task would allow one to effectively learn a full group representation from a partial set of well positioned detectors. We will propose such a mechanism, and briefly discuss its possible physical implementation.

Speaker: Xavier Fernández-Real (EPFL)
Title: The continuous formulation of shallow neural networks as Wasserstein-type gradient flows
Abstract: It has been recently observed that the training of a single hidden layer artificial neural network can be reinterpreted as a Wasserstein gradient flow for the weights for the error functional. In the limit, as the number of parameters tends to infinity, this gives rise to a family of parabolic equations. This talk aims to discuss this relation, focusing on the associated theoretical aspects appealing to the mathematical community and providing a list of interesting open problems.

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Speaker: Ángel González-Prieto (Universidad Complutense de Madrid)
Title: Generative Adversarial Networks for mathematicians
Abstract: Since their inception, Generative Adversarial Networks (GANs) have revolutionized the field of generative models due to their flexibility and ability to generate fully synthetic samples of very complex phenomena with high resolution. However, as they lie in the half-way between mathematics and engineering, sometimes is hard to unravel the mathematical properties of GANs and to translate them to implementations.
In this talk, we shall review the mathematical fundamentals of GANs, with special attention on how GANs are formulated as a competitive game and their optima as Nash equilibria. We will comment some of the known results about the convergence of GANs and their relation to the minimization of the Jensen-Shannon divergence and optimum transport problems.
Time permitting, we will discuss some of the recent developments in the study of the GAN convergence. In particular, we will focus on the interplay between the topology of the parameter space and the induced dynamical system, as well as the use of probabilistically inspired activation functions to improve the accuracy and convergence of GANs.
Joint work with A. Mozo, E. Talavera and S. Gómez-Canaval.

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Speaker: Peio Ibarrondo (UAM)
Title: Parabolic PDEs and neural network learning
Abstract: We discuss preliminary results on the construction of Fokker Planck equations describing the gradient flows associated to neural network learning of artificial data by backpropagation with prescribed architecture.

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Speaker: Jaime López (Repsol)
Title: TBA
Abstract: TBA

Speaker: Domenec Ruız-Balet. Universidad Autonoma de Madrid.
Title: Simultaneous control of Neural differential equations
Abstract: The contents of this lecture have been developed together with Enrique Zuazua. In this talk we will analyze the simultaneous controllability property of Neural differential equations. We will construct strategies for controlling N distinct data points to their corresponding targets for continuous time versions of residual neural networks (Resnets), momentum resnets and some models involving memory.

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Speaker: Enrique Zuazua. [1] Chair for Dynamics, Control and Numerics – Alexan-
der von Humboldt-Professorship FAU, Erlangen (Germany); [2] Chair of Computational
Mathematics, Fundacion Deusto, Bilbao; [3] Universidad Aut ́onoma de Madrid.
Title: Optimal control of deep neural networks
Abstract: We discuss the training process for Deep Neural Networks (DNN) from an optimal control perspective. In particular we analyze the turnpike phenomena, and how it emerges, as a function of the cost functional to be optimized, and that guarantees that, in the deep layer regime, the DNN experiences the tendency to become steady.
This lecture is inspired on recent joint work with Borjan Geshkovski, Carlos Esteve-Yagüe and Dario Pighin.

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Speaker: Carlos Castro. Universidad Politecnica de Madrid.
Title: Machine learning algorithms in inverse problems
Abstract: Neural networks software have serious difficulties to solve specific simple inverse problems concerning partial differential equations. We illustrate such difficulties and show how mathematical analysis for such problems can improve their efficiency

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Speaker: Francisco Periago. Universidad Politecnica de Cartagena.
Title: A first step towards numerical approximation of controllability problems via Deep-Learning-based methods
Abstract: This presentation is concerned with Deep-Learning-based algorithms for numerical approximation of controllability problems for PDEs. As a first step, and with the aim of having some feeling on accuracy of the proposed methods, two toy (low-dimensional) models for the heat and wave equations are considered. Error estimates
for generalization error are presented. Implementation details and numerical simulation results are showed. Finally, the extension of the proposed methods to high-dimensional problems is also discussed.

Speaker: Pedro Caro (BCAM)
Title: Discussing the paper “Convolutional neural networks in phase space and inverse problems” by G. Uhlmann and Y. Wang
Abstract: In this talk I will discuss the results on the paper “Convolutional neural networks in phase space and inverse problems” by G. Uhlmann and Y. Wang. The goal will be to analyse if some of these ideas could be transferred to the resolution of some other inverse problems.

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Speaker: Pablo Angulo (Universidad Politécnica de Madrid)
Title: Applying Neural ODE to inverse problems
Abstract: Neural ODE are the natural evolution of ResNets, allowing for very deep learning neural networks. Evaluation of the Neural ODE amounts to the numerical integration of a ODE system and the gradient of the loss function can be obtained through the adjoint method instead of backpropagation. We survey the applications of this technique to inverse problems, and the caveats that must be taken into account in order to get meaningful answers, with a special focus on continuous normalizing flows.

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Speaker: Luz Roncal (BCAM)
Title: Wavelet Analysis of the generalized Riemann non-differentiable Function
Abstract: We will report on recent progress done in showing the multifractality of a family of graphs that include Riemann non-differentiable function using wavelet analysis.

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Speaker: Angel González Prieto(UCM/ICEMAT)
Title: Recommender systems in action
Abstract: In the present-day information society, people are exposed to a massive amount of data from different sources. When we want to watch a series, the streaming platforms offer thousands of possibilities; when we want to travel abroad, search engines return hundreds of suitable flights with multiple companies; when we want to go out for dinner, innumerable restaurants are proposed through the booking platforms.
This continuous bombing of information is certainly overwhelming. To sort out this mess, recommender systems arose as machine learning models able to find the right item to be recommended to any user. Since their very inception, recommender systems have been a very active research area whose results have been quickly incorporated to almost all costumer-focused platforms such as Netflix, Spotify, Facebook, Amazon, Tinder…
In this talk, we will review the fundamental concepts and models of collaborative filtering-based recommender systems. This are state-of-art methods which, in a way or another, encode an inverse problem, namely, to extract the fundamental latent features of both users and items and to analyze how these hidden characteristics affect the recommendation. In particular, we shall focus on the main two approaches: matrix factorization-based systems and deep learning-based models, reaching some of our most recent proposals in both trends.
Joint work with Jesús Bobadilla, Raúl Lara-Cabrera and Fernando Ortega.

Speaker: Enrique Delgado Ávila
Title: Pressure stabilization in Reduced Order Methods for fluid flow problems
Abstract: In this work we present a Reduced Basis Model for a pressure stabilized Finite Element fluid flow. We perform the construction of an a posteriori error estimator for the selection of the basis functions via the Greedy algorithm, and we discuss the consideration of the inner pressure supremizer for the pressure recovery. In our model, we deal with some non-linearities that we solve in the Reduced Order framework with the Empirical Interpolation Method. Finally, we present some numerical results in which we show the speed-up in the computation of the reduced basis solution.

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Speaker: Samuele Rubino
Title: POD stabilized methods for incompressible flows: error analysis and computational results [joint work with Julia Novo (UAM)]
Abstract: Proper orthogonal decomposition (POD) stabilized methods for the Navier-Stokes equations are considered and analyzed. We consider two cases: the case in which the snapshots are based on a non inf-sup stable method and the case in which the snapshots are based on an inf-sup stable method. For both cases we construct approximations to the velocity and the pressure. For the first case, we analyze a method in which the snapshots are based on a stabilized scheme with equal order polynomials for the velocity and the pressure with local projection stabilization (LPS) for the gradient of the velocity and the pressure. For the POD method we add the same kind of LPS stabilization for the gradient of the velocity and the pressure as the direct method, together with grad-div stabilization. In the second case, the snapshots are based on an inf-sup stable Galerkin method with grad-div stabilization and for the POD model we also apply grad-div stabilization.
In this case, since the snapshots are discretely divergence-free, the pressure can be removed from the formulation of the POD approximation to the velocity. To approximate the pressure, needed in many engineering applications, we use a supremizer pressure recovery method. Error bounds with constants independent of inverse powers of the viscosity parameter are proved for both methods.
Numerical experiments show the accuracy and performance of the schemes, also combined with a data-driven approach.

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Speaker: David Pérez García
Title: Tensor Networks from a Quantum Information perspective
Abstract: I will introduce Tensor Networks and their use in quantum information and condensed matter physics. I will then review some of the main results and open problems.

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Speaker: Elias Cueto
Title: Mechanistic models and machine learning: friends or foes? [joint work with Quercus Hernández, Beatriz Moya, Alberto Badías, Iciar Alfaro, David Gonzalez, Francisco Chinesta]
Abstract: In this talk we will explore the interplay between well-known mechanistic physical laws and data science in the framework of the fourth paradigm of science. While the former have proved their success for centuries, they are also well-known to be difficult to distill, maintain, validate and apply, due to their inherent computational cost in many cases. In the last years we face an increasing interest in the leverage of the capabilities of data science to obtain predictive surrogates to these mechanistic models. However, the validity of these black-box surrogates is always under scrutiny: sensitivity to noise in the data, extrapolation capability, compliance with existing models, … It can be shown, however, that first principles can be easily incorporated into the learning machinery, giving rise to a promising family of techniques that satisfy by construction these physical laws. For instance, it is straightforward to impose a symplectic structure for systems that are conservative, thus leading to a learning procedure that guarantees energy conservation. But it is not so easy to develop learning methods for dissipative systems: what is the appropriate framework for them? We will show that imposing a metriplectic structure to the learning system guarantees the satisfaction of the laws of thermodynamics, thus opening the possibility of developing systems able to learn autonomously and still preserve the physics of the system under scrutiny. Examples will be show that prove the interest of such an approach.

Speaker: Tomás Chacón
Title: Certified Reduced order Large Eddy Simulation turbulence models. [joint work with Cristina Caravaca, Enrique Delgado Ávila and Macarena Gómez].
Abstract: This talk deals with the construction of reduced-order turbulence models with targeted error levels. We consider Large Eddy Simulation (LES) models of Smagorinsky kind, for which a complete mathematical and numerical analysis is known. This analysis allows the rigorous derivation of a-posteriori error estimators are built. On this basis, reduced basis are built by greedy algorithms, yielding error below targeted levels (certified method). We present the mathematical derivation of the a posteriori error estimators, as well as some application to benchmark flows as well as applications to thermal analysis of transition spaces in buildings.

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Speaker: Francisco Chinesta
Title: Hybrid Twins: Filling the gap between physics and data
Abstract: World is changing very rapidly. Today we do not sell aircraft engines, but hours of flight, we do not sell an electric drill but good quality holes, … We are nowadays more concerned by performances than by the products themselves. Thus, the new needs imply focusing on the real system subjected to the real loading that it experienced until the present time in order to predict the future responses and in this manner, anticipate any fortuity event or improve the performances.
Here, usual modeling and simulation techniques are limited because of the fact that a model is sometimes no more than a crude representation of the reality. Artificial Intelligence irrupted and became a major protagonist in many areas of technology and society at the beginning of the third millennium, however many times it requires impressive training efforts (incredible amount of data, most of them inexistent, difficult to collect and manipulate, extremely expensive in time and resources).
A highway to circumvent these difficulties and successfully accomplishing the most efficient (fast, accurate and frugal) generation of information and knowledge facilitating a real-time decision-making in engineering in general, and in forming processes in particular, consists of a hybrid paradigm combining real-time physics ad real-time physics-aware data-driven modelling.

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Speaker: J. Alberto Conejero
Title: Open Data Science Task Force against COVID-19: Winning the 500k XPRIZE Pandemic Response Challenge
Abstract: When the COVID-19 arrive to Spain, the Valencian Government created a Data Science Task Force to fight the pandemics, where the scientific community (through the Group of Experts) collaborate with the public administration (through the Commissioner at the level of the Presidency. After some time in which data was scarce and hard to obtain, we achieve to develop accurate computational epidemiological models that were complemented with human mobility studies, and information from a citizen survey called COVID19 impact survey.
Our work has received national and international recognition, including being the global winners of the 500k XPRIZE Pandemic Response Challenge, a four-month global competition organized by the XPRIZE Foundation. The challenge had two main goals: The first one was to foster the development of advanced AI models to forecast the evolution of the pandemics by combining different data sources. The second one was to prescribe Non-Pharmaceutical Intervention Plans that governments, business leaders and organizations could implement to minimize harm when reopening their economies. We will briefly describe these models and how information systems can feed these models to help against the pandemics.

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Speaker: Pablo Berna
Title: Lebesgue-type estimates for the Thresholding Greedy Algorithm
Abstract: The approximation theory with respect to bases in Banach spaces consists in the study of different ways to approximate a function by a finite linear combination of elements of that basis. The idea behind the non-linear approximation theory is that the elements used in the approximation do not come from a prefixed vector space. The Thresholding Greedy algorithm builds approximations of each function by selecting the largest coefficients (in absolute value) in the series expansion with respect to the basis. In that talk we present new results about the efficiency of the greedy algorithm.

Speaker: Eduardo García Portugués (Universidad Carlos III de Madrid) edgarcia@est-econ.uc3m.es
Title: Tests of hyperspherical uniformity based on chordal distances
Abstract: We provide a general and tractable family of tests of uniformity on the hypersphere of arbitrary dimension. The family is constructed from powers of the chordal distances between pairs of observations. The asymptotic null distributions of the new family of tests are obtained, as well as their explicit powers against sequences of generic local alternatives. The family of tests connects and extends three especially interesting particular cases. Numerical experiments corroborate the theoretical results.
Two real data applications on the two-dimensional sphere are given.

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Speaker: Rosa M. Crujeiras (Universidad de Santiago de Compostela) rosa.crujeiras@usc.es
Title: Complex regression for complex data
Abstract: There is a diverse range of practical situations where one may encounter random variables which are not defined on Euclidean spaces, as it is the case for circular data. Circular measurements may be accompanied by other observations, either defined on the unit circumference or on the real line, and in such cases it may be if interest to model the relationship between the variables from a regression perspective. It is not infrequent that parametric models fail to capture the underlying model given their lack of flexibility, but it may also happen that the usual paradigm of (classical) mean regression. We will present in this talk some recent advances in nonparametric multimodal regression, showing an adaptation of the mean-shift algorithm for regression scenarios involving circular response and/or covariate. Real data illustrations will be also presented. This is a joint work with María Alonso-Pena.

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Speaker: María Isabel Borrajo (Universidad de Santiago de Compostela) mariaisabel.borrajo@usc.es
Title: Kernel methods to cope with the analysis of point processes on road networks
Abstract: In this talk we explain a statistically principled method for kernel smoothing of point pattern data on a linear network when the first-order intensity depends on covariates. In particular, we present a consistent kernel estimator for the first-order intensity function that uses a convenient relationship between the intensity and the density of events location over the network, which also exploits the theoretical relationship between the original point process on the network and its transformed process through the covariate. The performance of the estimator is analysed through a simulation study under inhomogeneous scenarios. We also present a real data analysis on wildlife-vehicle collisions in a region of North-East of Spain.

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Speaker: Sara Prada (Clinipace WorldWide Company, Madrid) sara.prada.alonso@gmail.com
Title: Topological data analysis of high-dimensional correlation structures with applications in epigenetics
Abstract: There is currently a lack of standard and efficient analytical tools to deal with the great quantities and varieties of high-dimensional data as the genetic one. Particularly, the analysis of big-dimensional correlation structures is a pending topic in the epigenetics field. The topological analysis of the large and complex correlation structures contributes greatly to their understanding and interpretation.
Generally, the application of algebraic topology in data analysis through topological data analysis (TDA) provides with an efficient perspective, as the study and representation of the “shape” of the data is key to extract underlying data characteristics doing minimal prior assumptions about their distribution and reducing the dimension of the dataset.
Using the topological data analysis idea, our main proposal was to study the correlation of high-dimensional epigenetic datasets through the topological properties of the associated correlation networks or graphs, which represents a novel method to describe and model those structures. This analysis was done locally and globally to cover distinct complexity levels, designing different mathematical strategies and topological data analysis methodologies for each aim, as a computational algorithm called MultiNet. This algorithm is able to quickly represent the correlation structure and extract substantial information from it, as epigenetic patterns associated with a sample condition (as a disease).
This work opens the door to the application of these methodologies to other non-biological fields too.

Speaker: Aurea Grané (Universidad Carlos III de Madrid) agrane@est-econ.uc3m.es
Title: Smart visualization of mixed data
Abstract: In this work, we propose a new protocol that integrates robust classification and visualization techniques to analyze mixed data, which is based on the combination of the Forward Search Distance-Based (FS-DB) algorithm and robust clustering.
The methodology is illustrated on a real dataset related to European COVID-19 numerical health data, as well as policy and restriction measurements of the 2020-2021 COVID-19 pandemic across the EU Member States.

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Speaker: Lluís Belanche (Universitat Politècnica de Catalunya) luis.antonio.belanche@upc.edu
Title: Statistical learning of heterogeneous data: a case study and general ideas
Abstract: In data analysis, it is known that the chosen data representation is a crucial factor for a successful learning process, and yet current practice advocates for a change in representation, to adapt the data to the chosen modeling technique, instead of otherwise. Kernel methods offer a principled way for statistical learning when confronted with mixed data, even when faced with added difficult situations, like missing values. In this contribution we illustrate these assertions with the study of a difficult problem, the Horse Colic data set. Moreover, we give some advice and general ideas on the matter.

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Speaker: Beatriz Pateiro (Universidad de Santiago de Compostela) beatriz.pateiro@usc.es
Title: Sparse Matrix Classification on Imbalanced Datasets Using Convolutional Neural Networks
Abstract: In this work we deal with a class imbalance problem in the context of the automatic selection of the best storage format for a sparse matrix with the aim of maximizing the performance of the sparse matrix vector multiplication (SpMV) on GPUs. Our classification method uses convolutional neural networks (CNNs) trained using images that represent the sparsity pattern of the matrices, whose pixels are colored according to different matrix features. The experiments conducted show that our classifiers are able to select the best performing format 92.8% of the time, obtaining 98.3% of the maximum attainable SpMV performance. A comparison to other state-of-the-art classification methods is also provided, demonstrating the benefits of our proposal.

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Speaker: Virgilio Gómez-Rubio (Universidad de Castilla-La Mancha) Virgilio.Gomez@uclm.es
Title: Finding the optimal soccer player: spatial clustering applied to scouting
Abstract: Soccer teams face the problem of replacing players throughout the season. This is often due to injuries or some players leaving the team. Looking for new players is known as ‘scouting’ and it is a challenging problem as many times specific characteristics in the players are required, which means that a large number of characteristics need to be compared. From a statistical point of view, this problem can be tackled in a number of ways. If the desired player’s characteristics can be expressed as a (numerical) vector, then a distance can be defined so that the player with the smallest distance to the desired characteristics is the desired match. However, there are other restrictions that may apply such as players already under a contract, etc. One of the characteristics that defines a players role is the location in the field, as this is indicative of the main position within the team. Modern devices allow recording this position throughout the game, so that this can be exploited to develop ‘spatial profiles’ for the players. However, clustering these spatial profiles may be difficult due to a number of problems: (spatially) correlated data, different levels of spatial and temporal aggregation, etc. We have developed a novel way of exploiting location information about the players’ location in the field by means of spatial statistical methods. In particular, we have used an estimate of the time spent at every position in the field (obtained with a specific personal device) so that we can compare any two players by means of Lee’s test of spatial autocorrelation. The p-values obtained with these tests are then used as similarity functions in a hierarchical cluster so that different groups of players can be
identified. We have applied this method to more than 4000 soccer player’s profiles from different soccer leagues worldwide. (Joint work with Jesús Lagos, scoutanalyst.com [1] and Orange Spain, Madrid, Spain)

a. Iván Area, (U. de Vigo)
b. Elías Cueto, (U. de Zaragoza)
c. Daniel Djida, (AIMS)
d. Lukasz Plociniczak, (Wrocław University of Science and Technology)

Speaker: Cristina Rueda
Title: Mathematical and Statistical modeling using the FMM approach. The case of the Electrocardiogram.
Abstract: Oscillatory systems arise in the different biological and medical fields. Mathematical and statistical approaches are fundamental to deal with these processes.
The FMM approach, the acronyms refer to Frequency Modulated Mobius, reviewed here, is one of these approaches that competes with the Fourier and Wavelets decompositions. Little known as it has been recently developed, solves a variety of exciting questions with real data; some of them, such as the decomposition of the signal into components and their multiple uses, are of general application others are specific. Among the exciting specific applications is the FMMecgmodel that solves the forward and reverse problem in electrocardiography providing a sound automatic interpretation method of the ECG signal.

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Speaker: José Enrique Chacón
Title: Modal clustering asymptotics
Abstract: In nonparametric density-based clustering, clusters are understood as regions of high concentration of probability mass, separated from each other by regions of lower density. Therefore, clusters are naturally associated to density modes and this approach is called modal clustering. The population goal of modal clustering can thus be defined in terms of the domains of attraction of the true density modes, and that allows framing the clustering problem in a standard inferential setting. In this talk we show some recent results concerning the asymptotic properties of data-based modal clusterings, constructed via the usual plug-in methodology, employing a density estimator. Limit theorems are shown for the unidimensional case, but their multivariate extensions stand out as a challenging open problem.

Speaker: Aritz Pérez
Title: Identificación de redes de suministro de energía eléctrica empleando algoritmos de optimización combinatoria
Abstract: La energía eléctrica se transfiere entre proveedores y consumidores empleando una red de distribución de energía. Dicha red es cambiante a lo largo del tiempo debido a que los consumidores pueden cambiar de proveedor. Debido a la naturaleza cambiante de la red de distribución, se desconocen las conexiones entre proveedores y consumidores de energía eléctrica. En el proyecto realizado junto a Ormazabal S.L. hemos reformulado el problema de la identificación de la red de suministro como un problema de optimización combinatoria. El problema de optimización consiste en asociar a cada consumidor un único proveedor de manera que se minimice la diferencia entre la energía consumida y la producida por cada proveedor. El problema de optimización se ha abordado empleando algoritmos genéticos y la búsqueda local, así como diversas variantes de los mismos.

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Speaker: Francisco Parreño
Coauthor: Ramón Álvarez-Valdés
Title: Solving a large cutting problem in the glass manufacturing industry
Abstract: The two-dimensional glass cutting problem to be solved by Saint Gobain, one of the world’s largest producers of flat glass, includes some specific constraints that
prevent the direct application of procedures developed for the standard cutting problem. On the one hand, the sheets to be cut have defects that made them unique and must be used in a specific order. On the other hand, the pieces are grouped in stacks and the pieces in each stack must be cut in order. There are also some additional characteristics due to the technology used, especially the requirement for a three-staged guillotine cutting process. We have developed heuristic and exact procedures. First, we have developed a beam search algorithm, using a tree structure in which at each level the partial solution is augmented by adding some new elements until a complete solution was built. We developed a randomized constructive algorithm for building these new elements and explored several alternatives for the local and the global evaluation. An improvement procedure, specifically designed for the problem, was also added. The computational study, using the datasets provided by the company, shows the efficiency of the proposed algorithm for short and long running times.

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Speaker: Francisco Parreño
Coauthor: Ramón Álvarez-Valdés
Title: Grid operation-based outage maintenance planning
Abstract: RTE (Réseau de Transport d’Électricité) is the operator of the French electricity transmission system, with a network of 100,000 km. When planning maintenance
operations, some interventions require the power supply to be cut off. When this happens, the electricity supply must be guaranteed, so maintenance operations must be carefully planned. To tackle this issue, RTE decided to apply a three-step approach. First, risk values are calculated for different future scenarios. Second, these computed values are used to find a good schedule. Eventually, a third step validates the obtained planning. Our optimization problem arises in the second step of this approach: given the risk values, the goal is to find an optimal planning regarding a risk-based objective. Moreover, this planning must be consistent with all job-related restrictions such as resource constraints. The objective function includes the average risk, over time and scenarios, and a measure of the cost variability, expressed by a quantile of the
risk distribution. Our approach generates first a set of good solutions by solving integer linear models whose objective functions are approximations of the actual objective of the problem. These solutions then go through an improvement phase, which includes a Variable Neighborhood Search and a Path Relinking algorithm. The computational study, on a set of instances provided by the company, shows that the complete procedure obtains high quality solutions.

Speaker: María Jose Cano Vicente (Vócali)
Title: Speech recognition in legal and medical contexts
Abstract: Transcribing audio speech segments into text has many different applications these days. From simple transcription, subtitles, keyword searching in media files… to dictation, human machine interaction and all kinds of Natural Language Processing applications. All of them have in common necessities and difficulties. Speech recognition is typically based on different pieces to model different parts of the natural understanding process of a speech. It is necessary to process sound into a numerical representation that allows associate frames of audio to elemental parts. If these elemental parts are phonemes, they must be bundled into words among a lexicon, which can
be general or very specific to the domain. To enhance probability of choosing word combinations into coherent phrases also language is modelled. Several approximations are making good results over last years. Classical approximations are improving their performances even in low resources cases, as well as new varieties of sequence-to-sequence models are integrating the latest advances in neural networks architectures. Vócali INVOXMedical and INVOXLegal make it possible to adapt these speech recognition
technologies into domain-specific lexicons and speech contexts.

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Speakers: Alexandra Aguilar Torres & Jesús Alberto Villa Diez (Airbus DS)
Title: How Natural Language Processing is helping in Defence and aerospace
Abstract: Airbus is a global aerospace-and-defense corporation known for developing military and commercial aircraft. Like many other traditional industries, it is delving into a digital. In this process, AI and machine learning techniques are playing a key role. We’ll provide an overview of the different areas where NLP techniques are being used at Airbus Defense & Space, their benefits, challenges and lessons learnt, as well as the technical approach for one of our projects.

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Speaker: Víctor Gallego Alcalá (ICMAT & Komorebi AI)
Title: Zero-shot learning in extremely large Transformer models (GPT and CLIP). Mathematical and computational aspects
Abstract: The rise of neural models such as BERT, GPT-3 or CLIP, trained on huge amounts of data at scale, has led an undergoing paradigm shift in Artificial Intelligence. These deep learning models, leveraged with transfer learning, have been proved to be adaptable to a wide range of downstream tasks in both Natural Language Processing and Computer Vision. Traditionally, models were pretrained on a large corpus of data, and then fine-tuned on a specific dataset and task. However, scaling up language models vastly improves few- (and zero-)shot performance in different tasks, sometimes reaching comparable results to the state of the art. In this talk, after reviewing the underlying the mathematical aspects of these models, we will showcase several approaches towards zero/few-shot learning, such as prompt engineering or prompt tuning. Then, we will show several industrial applications, like text generation for content creation and SEO optimization, and semantic search for navigating large datasets of raw, non-annotated images. A demo of the language model can be found at http://api.vicgalle.net:8000/

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Speaker: María Medina (Microsoft)
Title: MLOps: how to operationalize your Machine Learning systems
Abstract: Beyond experimentation, data science projects can evolve into very complex systems with strong maintenance requirements. No matter how good our models are, these systems will end up abandoned and the models not being used at all if we don’t apply good practices in Software Engineering when putting them into production. But the data science world has some very unique characteristics, different from traditional software development, that have to be considered in the design of this operationalization process. In this talk we’ll go through the most important DevOps practices and learn how they are applied by industries in data science projects to build end-to-end pipelines for automatic training and deployment of machine learning models.