A tutorial at the ACM conference on Economics and Computation 2023
The tutorial is aimed to present a novel procedure for model robustness validation. The key idea is to apply deep generative models that can sample unlikely events and introduce slight shifts in input data. Such an approach allows us to examine models’ reactions to input data with different levels of the likelihood. The growing number of running models highlights the importance of model validation, which determines the process of verifying the validity of input and output data, model’s performance, stability, and interpretability.
The target audience is risk managers and researchers who are interested in applying deep generative models for evaluating the model robustness. We assume the audience has basic knowledge in machine learning, probability theory, statistics and python programming.
Tutorial part 1: Background (10:30am-11:15am ET), Notebook
The definition of data distribution shifts. The worst-case risk approach to estimate the potential decrease in the target metric.
Tutorial part 2: Deep generation of stress data (12:00pm-12:45pm ET), Notebook
Considering deep generative models: Generative Adversarial Networks. Discussion of advantages and disadvantages of deep generative models.
Artificial Intelligence Research Institute (AIRI), Moscow, Russia.
Vitaliy Pozdnyakov, researcher
Vitaliy Pozdnyakov has 7 years of experience in industrial companies as a developer of enterprise resource planning systems and 3 years in scientific research of industrial artificial intelligence methods. His master's thesis is devoted to probabilistic forecasting of multidimensional time series using deep generative models.
Dmitrii Kiselev, researcher
Dmitry Kiselev is a PhD with a solid engineering and consulting background applying machine learning and data science techniques to production in travel and financial industries.
Alexander Kovalenko, researcher
Alexander Kovalenko is an engineer specializing in microelectronics and solid-state electronics. He has more than 12 years of experience working with industrial equipment as an electronics engineer. His main area of expertise is applications of machine learning in industry, such as graph neural networks for fault detection and diagnosis based on data from multiple sensors.