The first meeting took place on 27 February 2014 at the ICM UW. Since then, our R community has grown a lot!
We would like to celebrate the 5th anniversary together.
We will start with a talk by Professor Marko Robnik-Šikonja about local explanations of machine learning models.
18:00-18:05 - Welcome
18:05 - 18:45 - Perturbation based explanations of ML predictions - Marko Robnik-Šikonja
18:45-... - Celebration with cake
Current research into algorithmic explanation methods for predictive models can be divided into two main approaches: gradient-based approaches limited to neural networks, and more general perturbation-based approaches which can be used with arbitrary prediction models. We present an overview of perturbation-based approaches, with focus on popular methods (EXPLAIN, IME, LIME, SHAP). These methods support explanation of individual predictions but can also visualize the model as a whole. We describe their working principles, how they handle computational complexity, their visualizations as well as their advantages and disadvantages. We illustrate issues and challenges in applying the explanation methodology on practical use cases.
Marko Robnik-Šikonja is Professor of Computer Science and Informatics and Head of Artificial Intelligence Chair at University of Ljubljana, Faculty of Computer and Information Science. His research interests span machine learning, data mining, knowledge discovery in databases, cognitive modelling, natural language processing and application of data mining techniques. His most notable scientific results concern feature evaluation, ensemble learning, network analysis, model and prediction explanation, generation of semi-artificial data, and natural language analysis. He is (co)author of more than 100 scientific publications and three open source R data mining packages.
After the talk, we will celebrate the 5th anniversary of SER/RUG with a cake.
The event will be sponsored by Appsilon.
Appsilon delivers the most advanced R Shiny apps, data science consulting services and support with R Shiny and Python Dash technologies.
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