We plan to dedicate the 2023 – 2025 series of conferences to challenges and emerging topics in the area of mathematical statistics driven by the adventure of artificial intelligence. Tremendous progress has been made in building up powerful machine learning algorithms such as random forests, gradient boosting or neural networks. These models are exceptionally complex and difficult to interpret but offer enormous opportunities in many areas of application going from science, public policies to business. These sophisticated algorithms are often called “black boxes” as they are very hard to analyze. The widespread use of such predictive algorithms raises extremely important questions of replicability, reliability, robustness or privacy protection. The proposed series of conferences is dedicated to new statistical methods built around these black-box algorithms that leverage their power but at the same time guarantee their replicability and reliability.

The second conference of the program will highlight recent theoretical advances in inference for high-dimensional statistical models based on the interplay of techniques from mathematical statistics, machine learning and theoretical computer science. The importance of high-dimensional statistics is due to the increasing dimensionality and complexity of models needed to process and understand modern data. Meaningful inference about such models is possible assuming suitable lower dimensional underlying structure or low-dimensional approximations, for which the error can be reasonably controlled. Examples of such structures include sparse high dimensional regression, low rank matrix models, dictionary learning, network models, latent variable models, topic models, and others.