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 quantum computers. In the lecture I will describe some questions about mathematics, statistics and computational complexity which arose in my study of NISQ systems and […]
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 at an intermediate model complexity, striking a balance between simpler models that exhibit high bias and more complex models that exhibit high variance of the […]
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 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. […]
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 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 […]
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 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 […]
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 technology shocks or monetary policy shocks to produce these aggregate fluctuations. In this paper, we show that a state-dependent pricing model with a large but […]
Universes as Big Data, or Machine-Learning Mathematical Structures
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 […]
Members’ Seminar
The CMSA Members’ Seminar will occur every Friday at 9:30am ET on Zoom. All CMSA postdocs/members are required to attend the weekly CMSA Members’ Seminars, as well as the weekly CMSA Colloquium series. Please email the seminar organizers to obtain a link. This year’s seminar is organized by Tianqi Wu. The Schedule will be updated below. […]
Machine learning and su(3) structures on six manifolds
Speaker: James Gray - Virginia Tech Title: Machine learning and su(3) structures on six manifolds Abstract: In this talk we will discuss the application of Machine Learning techniques to obtain numerical approximations to various metrics of SU(3) structure on six manifolds. More precisely, we will be interested in SU(3) structures whose torsion classes make them suitable […]
The Inside View: Raymarching and the Thurston Geometries
On Wednesday, December 16 at 12:00 p.m. EST, WAM and CMSA will host a holiday seminar featuring Sabetta Matsumoto, Georgia Institute of Technology who will present The Inside View: Raymarching and the Thurston Geometries. The properties of euclidean space seem natural and obvious to us, to the point that it took mathematicians over two thousand years to […]