While all research and data collections in the past year has been focused on the damaging impact of Covid19 on the national and international level, as an unprecedented phenomenon that is greater in its negative magnitude than the great depression of 1929-33, this project is centered around the other side of the coin. We will…
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…
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…
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,…
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…
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…
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…
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…
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…
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…
