{"id":53807,"date":"2024-11-25T16:49:23","date_gmt":"2024-11-25T16:49:23","guid":{"rendered":"https:\/\/www.iseg.ulisboa.pt\/?post_type=event&p=53807"},"modified":"2024-11-27T12:26:20","modified_gmt":"2024-11-27T12:26:20","slug":"mathematical-optimization-in-explainable-machine-learning-seminar","status":"publish","type":"event","link":"https:\/\/www.iseg.ulisboa.pt\/en\/event\/mathematical-optimization-in-explainable-machine-learning-seminar\/","title":{"rendered":"Seminar | Mathematical Optimisation in Explainable Machine Learning"},"content":{"rendered":"

On 4th December, 2024<\/strong>, CEMAPRE (Centre for Applied Mathematics for Economic Forecasting and Decision Making) will hold a seminar on 'Mathematical Optimisation in Explainable Machine Learning'<\/strong>, presented by Emilio Carrizosa<\/strong>, a professor at the University of Seville. The event will take place in ISEG's Lecture Theatre 3 <\/strong>(ISEG, Quelhas Building), from 14.30 to 15.30<\/strong>.<\/p>\n\n\n\n

The seminar will address the key role of mathematical optimisation in the development of explainable machine learning<\/em> models. Although traditional explainable machine learning<\/em> models are widely used in technology, science, and decision-making, they are often seen as 'black boxes'. This characteristic can limit their acceptance and social support, leading to the emergence of new models within the concept of Explainable Machine Learning<\/em><\/strong>.<\/p>\n\n\n\n

According to Emilio Carrizosa, mathematical modelling and optimisation tools are not only essential for traditional methods of explainable machine learning<\/em>, but they also have even more relevance in the explainable context. This is because they make it possible to integrate desirable properties, such as thrift<\/strong> and justice<\/strong>, through constraints or penalty terms in optimisation problems.<\/p>\n\n\n\n

The presentation will highlight two specific challenges that the speaker's research team has been working on:<\/p>\n\n\n\n