https://youtu.be/aVB1qFPeEmo Speakers: Boaz Barak and Yamini Bansal, Harvard University Dept. of Computer Science Title: Generalization bounds for rational self-supervised learning algorithms, or "Understanding generalizations requires rethinking deep learning" Abstract: The generalization gap of a learning algorithm is the expected difference between its performance on the training data and its performance on fresh unseen test samples. […]

https://youtu.be/uUUeTYzMu0Q Speaker: Florent Krzakala, EPFL Title: Some exactly solvable models for machine learning via Statistical physics Abstract: The increasing dimensionality of data in the modern machine learning age presents new challenges and opportunities. The high dimensional settings allow one to use powerful asymptotic methods from probability theory and statistical physics to obtain precise characterizations and […]

https://youtu.be/t4xRwWxTzSg Speaker: Eric Mjolsness, Departments of Computer Science and Mathematics, UC Irvine Title: Towards AI for mathematical modeling of complex biological systems: Machine-learned model reduction, spatial graph dynamics, and symbolic mathematics Abstract: The complexity of biological systems (among others) makes demands on the complexity of the mathematical modeling enterprise that could be satisfied with mathematical […]

https://youtu.be/zj_Xc2QG-vw Speaker: Yang-Hui He, Oxford University, City University of London and Nankai University Title: Universes as Big Data, or Machine-Learning Mathematical Structures Abstract: We review how historically the problem of string phenomenology lead theoretical physics first to algebraic/differetial geometry, and then to computational geometry, and now to data science and AI. With the concrete playground […]