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.
The second edition of I-X Breaking Topics in AI conference sponsored by Schmidt Sciences.
This presentation will showcase recent advancements in micro-robotic systems, focusing on the innovative application of non-contact optical manipulation using Optical Tweezers (OT).
This talk discusses rough path theory, an area of mathematics that fuses the control theory of Sussmann, Brockett and Fleiss with the analysis of Young to form a calculus that can efficiently describe the interaction and evolution of complex oscillatory systems.
