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Among natural disasters, seismic activity which can cause earthquakes is a serious risk for human activities and most importantly their lives. Seismic risk assessment requires knowledge to evaluate existing buildings and their expected response against ear ...
The modeling of the probability of joint default or total number of defaults among the firms is one of the crucial problems to mitigate the credit risk since the default correlations significantly affect the portfolio loss distribution and hence play a sig ...
We develop a methodology to measure the expected loss of commercial banks in a market downturn, which we call stressed expected loss (SEL). We simulate a market downturn as a negative shock on interest rate and credit market risk factors that reflect the b ...
This thesis consists of three applications of machine learning techniques to risk management. The first chapter proposes a deep learning approach to estimate physical forward default intensities of companies. Default probabilities are computed using artifi ...
In this dissertation, I develop theory and evidence to argue that new technologies are central to how firms organize to create and capture value. I use computational methods such as reinforcement learning and probabilistic topic modeling to investigate thr ...
The effects of robotics and artificial intelligence (AI) on the job market are matters of great social concern. Economists and technology experts are debating at what rate, and to what extent, technology could be used to replace humans in occupations, and ...
Autonomous mobility devices such as transport, cleaning, and delivery robots, hold a massive economic and social benefit. However, their deployment should not endanger bystanders, particularly vulnerable populations such as children and older adults who ar ...
The purpose of this article is to develop and study a decentralized strategy for Pareto optimization of an aggregate cost consisting of regularized risks. Each risk is modeled as the expectation of some loss function with unknown probability distribution, ...
Using artificial intelligence to improve patient care is a cutting-edge methodology, but its implementation in clinical routine has been limited due to significant concerns about understanding its behavior. One major barrier is the explainability dilemma a ...
We discuss the use of likelihood asymptotics for inference on risk measures in univariate extreme value problems, focusing on estimation of high quantiles and similar summaries of risk for uncertainty quantification. We study whether higher-order approxima ...