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 21/3: Otaviano Canuto (Policy Center for the New South) – Lost in transition: developing countries in the global economy

developing

The growth and productivity performance of emerging market and developing economies since the 2008 global financial crisis failed to repeat the achievements of the previous decade. Besides frustrating expectations that they might become the new growth pole in the global economy, their convergence to per capita incomes of advanced economies has suffered a setback. Nonetheless,…

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 21/1:Shashiryar Nasirov (Universidad Adolfo Ibáñez, Santiago de Chile): Decarbonization of the Chilean Power Sector: A Dynamic General Equilibrium Modeling Analysis

Line of Turbines

A rapid expansion of renewables in the Chilean energy matrix, mostly thanks to exceptional solar and wind resources, combined with a rapid decrease in the cost of renewable energy technologies, intensified current policy debates to reduce the role of coal, which is the largest source of CO2 emissions in the generation mix. Recently, the main…

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 20/24: Francesco Ciardiello (Sheffield University: On Pure-Strategy Nash Equilibria in a Duopolistic Market Share Model

negotiation

This paper develops a duopolistic discounted marketing model with linear advertising costs and advertised prices for mature markets still in expansion. Generic and predatory advertising effects are combined together in the model. We characterize a class of advertising models with some lowered production costs. For such a class of models, advertising investments have a no-free-riding…

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 20/22: David Ríos (Real Academia de Ciencias), Adversarial Machine Learning: Perspectives from Adversarial Risk Analysis

Machine_learning

Adversarial Machine Learning (AML) is emerging as a major field aimed at the protection of automated ML systems against security threats. The majority of work in this area has built upon a game-theoretic framework by modelling a conflict between an attacker and a defender. After reviewing game-theoretic approaches to AML, we discuss the benefits that…

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…