Seminario 21/7: Mikel Sesma (Universidad de Navarra): Directional monotonicity and related concepts in the context of data aggregation

data

In the field of Information Fusion, and, more particularly, data aggregation, the process of aggregation deals with the problem of finding a single number that is able to represent an input of n numbers. In this framework, there is a trend towards relaxing the monotonicity conditions that are often required so that a function can…

Seminario 21/5: Domingo Morales (UMH) Mejor predicción empírica de parámetros bivariados de áreas pequeñas

bivariate

La charla introduce predictores óptimos empíricos de parámetros bivariados de área pequeña, como razones de sumas o sumas de razones, asumiendo que el vector objetivo a nivel de unidad sigue un modelo de regresión de errores anidados bivariados. Los correspondientes errores cuadráticos medios se estiman mediante bootstrap paramétrico. Varios experimentos de simulación estudian empíricamente el…

Jose Manuel Cordero

Seminario 21/4: Jordi Pons i Puig (Dolby Laboratories): Deep learning architectures for music audio classification: a personal (re)view

descarbonization

A brief review of the state-of-the-art in music informatics research and deep learning reveals that such models achieved competitive results for several music-related tasks. In this talk I will provide insights in which deep learning architectures are (according to our experience) performing the best for audio classification. To this end, I will first introduce a…

Seminario 21/2: Magdalena Kapelko (Wroclaw University of Economics and Business in Poland): Assessing Corporate Social Responsibility Efficiency for the International Food and Beverage Manufacturing Industry

descarbonization

One of themajor challenges in the research on corporate social responsibility (CSR) isthe aggregation of the CSR metrics into overall measures of CSR practices byfirms. The paper computes composite indicators of CSR from an efficiencyperspective using data envelopmentanalysis (DEA) for a sample of international food and beveragemanufacturing firms over the period 2011-2018. The study’s contributions…

Seminario 20/25: Dolores Romero Morales (Copenhagen Business School, Denmark): Explaining Machine Learning Outcomes by Means of Mathematical Optimization

white-red-and-yellow-abstract-painting

There is a growing literature on enhancing the interpretability of Machine Learning methods involved in Data Driven Decision Making. Interpretability is desirable for non-experts; it is required by regulators for models aiding, for instance, credit scoring; and since 2018 the European Union has extended this requirement by imposing the so-calledright-to-explanation in algorithmic decision making. Mathematical…

Seminario 20/23: Laureano F. Escudero (Universidad Rey Juan Carlos), On the Stochastic Dominance functional-basedrisk averse versions in mathematical optimization under uncertainty

Ecuaciones diferenciales

Very frequently, mainly in dynamic problems,some data is uncertain at the decision-making time, although some information is already available. The mathematical optimization models under uncertainty, so-named stochastic optimization ones structure the uncertainty in a set of representative scenarios. The stochastic Risk Neutral (RN) models aim to obtaining a feasible solution for the scenario-based constraint system…

Seminario 20/21: Dae-Jin Lee (Centro Vasco de Matemática Aplicada, BCAM): Ciencia de datos, tecnología y deporte: retos y experiencias desde la perspectiva de la investigación matemática

deporte

En esta charla se introducirá un campo emergente como es la ciencia de datos en el deporte y su creciente relevancia en el rendimiento deportivo, la prevención de lesiones o el análisis técnico-táctico en deportes de equipo. Se plantearán una serie de cuestiones importantes relacionadas con esta nueva profesión y la ciencia detrás de la…

Seminario 20/20: Virgilio Gómez-Rubio (Universidad de Castilla-La Mancha): Sistemas de información epidemiológicos para análisis de datos

epidemia

La pandemia de la COVID-19 ha puesto de manifiesto la necesidad de contar con sistemas de información epidemiológicos robustos que puedan ayudar en el análisis de datos para la toma de decisiones. Además, de almacenar y gestionar la información, un buen sistema de información epidemiológico debe ser capaz de explotar esa información para realizar análisis…

Laureano Escudero (Universidad Rey Juan Carlos): On the Stochastic Dominance functional-basedrisk averse versions in mathematical optimization under uncertainty

Very frequently, mainly in dynamic problems,some data is uncertain at the decision-making time, although some informationis already available. The mathematical optimizationmodels under uncertainty, so-named stochastic optimization ones structurethe uncertainty in a set of representative scenarios. The stochastic RiskNeutral (RN) models aim to obtaining a feasible solution for the scenario-basedconstraint system that, say, maximizes the expected…