Graph Representation Learning: Recent Advances and Open Challenges
Speaker: William Hamilton, McGill University and MILA Title: Graph Representation Learning: Recent Advances and Open Challenges Abstract: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum […]
Self-induced regularization from linear regression to neural networks
https://youtu.be/bjRqmlI_SFs Speaker: Andrea Montanari, Departments of Electrical Engineering and Statistics, Stanford Title: Self-induced regularization from linear regression to neural networks Abstract: Modern machine learning methods --most noticeably multi-layer neural networks-- […]
Statistical, mathematical, and computational aspects of noisy intermediate-scale quantum computers
Speaker: Gil Kalai (Hebrew University and IDC Herzliya) Title: Statistical, mathematical, and computational aspects of noisy intermediate-scale quantum computers Abstract: Noisy intermediate-scale quantum (NISQ) Computers hold the key for important theoretical and experimental questions regarding […]
Triple Descent and a Fine-Grained Bias-Variance Decomposition
Speaker: Jeffrey Pennington, Google Brain Title: Triple Descent and a Fine-Grained Bias-Variance Decomposition Abstract: Classical learning theory suggests that the optimal generalization performance of a machine learning model should occur […]
Generalization bounds for rational self-supervised learning algorithms, or “Understanding generalizations requires rethinking deep learning”
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 […]
Some exactly solvable models for machine learning via Statistical physics
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 […]
Towards AI for mathematical modeling of complex biological systems: Machine-learned model reduction, spatial graph dynamics, and symbolic mathematics
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 […]
Re-pricing avalanches
Speaker: Jose A. Scheinkman (Columbia) Title: Re-pricing avalanches Abstract: Monthly aggregate price changes exhibit chronic fluctuations but the aggregate shocks that drive these fluctuations are often elusive. Macroeconomic models often add stochastic macro-level shocks such as […]