Talk, Mathematics Seminar, Khalifa University, Abu Dhabi
In this talk, we will delve into the asymptotic study of simple linear generative models when both the sample size and data dimension grow to infinity. In this high-dimensional regime, random matrix theory (RMT) appears to be a natural tool to assess the model’s performance by examining its asymptotic learned conditional probabilities, its associated fluctuations, and the model’s generalization error. This analytical approach not only enhances our comprehension of generative language models but might also offer novel insights into their refinement through the lens of high-dimensional statistics and RMT. [slides]
In this talk, we will present an asymptotic analysis of large asymmetric spiked tensor models, relying on tools from random matrix theory. In the first part of the talk, we will provide the analysis of a rank-one model along with some algorithmic implications in terms of possible signal recovery with a polynomial time algorithm. The second part of the talk will present a generalization to a low-rank spiked model with non-independent components, by studying two types of deflation procedures and proposing an improved algorithm. [slides] [video]
Talk, New York University Abu Dhabi, Abu Dhabi, UAE
In this talk, we will present an asymptotic analysis of large asymmetric spiked tensor models, relying on tools from random matrix theory. In the first part of the talk, we will provide the analysis of a rank-one model along with some algorithmic implications in terms of possible signal recovery with a polynomial time algorithm. The second part of the talk will present a generalization to a low-rank spiked model with non-independent components, by studying two types of deflation procedures and proposing an improved algorithm. [slides]
Talk, Lagrange Center (Huawei Technologies France), Paris, France
In this talk, we will present an asymptotic analysis of large asymmetric spiked tensor models, relying on tools from random matrix theory. In the first part of the talk, we will provide the analysis of a rank-one model along with some algorithmic implications in terms of possible signal recovery with a polynomial time algorithm. The second part of the talk will present a generalization to a low-rank spiked model with correlated components, by studying a Hotteling-type deflation procedure. [slides]
Tutorial, European Signal Processing Conference (EUSIPCO), A Coruna, Spain
Kernel random matrices and applications (analysis and improvement) to sampling, kernels and sparse covariance estimation, spectral clustering, semi-supervised learning, random feature maps, neural networks, universality considerations. [slides]
