This workshop aims to bring together researchers in stochastic analysis, statistics and theoretical machine learning for an exchange of ideas at the forefront of the field. The
This talk introduces the Reality-Centric AI agenda, an approach that tackles the complexity and challenges of the real world through machine learning (ML).
This talk will show how to build neural models that are not only guaranteed to be compliant with the given requirements over the output space, but are also able to learn from the background knowledge expressed by the requirements themselves and thus get better performance.
This talk will present the core methodologies and techniques for deep learning with graph-structured data along with some recent advances and open problems.
In this tutorial, we dive into this framework to learn how to: Formalize the DG setting as a robust optimization problem; Describe the training and shifted test distributions with structural causal models (SCMs); Show that causal models work well when the training and test distribution differ; Estimate causal models from training data to achieve DG.
In this talk, Roberto and his team discuss learning and inference problems in the quantum world and how these problems differ from their classical counterparts.
This talk will provide a brief overview of the growing field of operator learning and see how numerical linear algebra algorithms, such as the randomized singular value decomposition, can be exploited to gain theoretical and mechanistic understanding of operator learning architectures.
