-
Adela DePavia (The University of Chicago)5/18/26, 11:00 AMNumerical Linear Algebra in Machine LearningMinisymposium Talk
Adaptive gradient optimization algorithms—including Adam, Adagrad, and their variants—have found widespread use in machine learning, signal processing, and many other settings. However many algorithms in this family are not rotationally equivariant: in this talk we examine how a simple change-of-basis in either parameter space or data space can drastically impact both the convergence rates and...
Go to contribution page -
Kaustubh Roy (University of Manchester)5/18/26, 11:25 AMNumerical Linear Algebra in Machine LearningMinisymposium Talk
The CLASSIX algorithm is a fast and explainable approach to data clustering. In its original form, this method utilizes the first principal component of the data matrix to truncate the search for nearby data points, using the Cauchy-Schwarz inequality, with proximity being defined in terms of the Euclidean distance. In this work, we demonstrate methods to extend CLASSIX to other distance...
Go to contribution page -
Yanfei Xiang (University of Strasbourg)5/18/26, 11:50 AMNumerical Linear Algebra in Machine LearningMinisymposium Talk
This work exclusively focuses on the mixed precision algorithms that integrate classical numerical linear algebra methods with nonlinear neural network-based preconditioners to accelerate the solution of some
Go to contribution page
parametric Partial Differential Equations (PDEs). Specifically, we consider Krylov subspace methods such as Flexible GMRES (FGMRES) and Flexible FOM (FFOM), combined with nonlinear or... -
James Hazelden (University of Washington)5/18/26, 2:00 PMNumerical Linear Algebra in Machine LearningMinisymposium Talk
How are learned representations incrementally formed to solve tasks by Gradient Descent (GD)? In this talk, we will show that each step of GD is exactly given by the application of a massive tensor-valued linear operator, which we call the Configuration Space Neural Tangent Kernel (NTK). We prove that it can be decomposed into two operators: P and K, the former capturing state-to-state...
Go to contribution page -
Haoran Ni (University of Warwick)5/18/26, 2:25 PMNumerical Linear Algebra in Machine LearningMinisymposium Talk
Normalizing Flows (NFs) enable tractable density evaluation by modelling data through invertible neural transformations. However, this reliance on global bijectivity severely restricts their expressiveness when the target distribution lies on a low-dimensional manifold or exhibits complex topology. To overcome this limitation, we introduce Principal Surjective Flows (PSFs), a framework that...
Go to contribution page -
Eda Oktay (Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg)5/18/26, 2:50 PMNumerical Linear Algebra in Machine LearningMinisymposium Talk
As machine learning and AI continue to shape modern hardware design, reduced-precision arithmetic has become essential in high-performance computing. Recent advances in hardware architectures—such as AI accelerators, GPUs, and tensor-core technologies—many of which are driven by machine-learning workloads, are optimized for low-precision operations to improve performance and reduce energy...
Go to contribution page -
Kathryn Lund (STFC Scientific Computing)5/18/26, 3:45 PMNumerical Linear Algebra in Machine LearningMinisymposium Talk
The tensor t-function, a formalism that generalizes the well-known concept of matrix functions to third-order tensors, is introduced in Lund (Numer Linear Algebra Appl 27(3):e2288). In this work, we investigate properties of the Fréchet derivative of the tensor t-function and derive algorithms for its efficient numerical computation. Applications in condition number estimation and nuclear...
Go to contribution page -
Mantas Mikaitis (University of Leeds)5/18/26, 4:10 PMNumerical Linear Algebra in Machine LearningMinisymposium Talk
Matrix multiplication is a fundamental operation in both training of neural networks and inference. To accelerate matrix multiplication, Graphical Processing Units (GPUs) provide it implemented in hardware. Due to the increased throughput over the software-based matrix multiplication, the multipliers are increasingly used outside of AI, to accelerate various applications in scientific...
Go to contribution page -
Xiaobo Liu (Max Planck Institute for Dynamics of Complex Technical Systems)5/18/26, 4:35 PMNumerical Linear Algebra in Machine LearningMinisymposium Talk
Reduced rank extrapolation (RRE) is a classic acceleration method for vector-valued fixed-point process, commonly airsing from iterative solution of algebraic equations. In this talk, we discuss the generalization of this extrapolation framework to sequences of low-rank matrices generated by iterative methods for large-scale matrix equations, such as low-rank alternating directions implicit...
Go to contribution page -
Stanislav Budzinskiy (University of Vienna)5/18/26, 5:00 PMNumerical Linear Algebra in Machine LearningMinisymposium Talk
We address the floating-point computation of compositionally-rich functions, concentrating on LLM inference. Based on the rounding error analysis of a composition, we provide an adaptive strategy to select components of the inner function that need to be recomputed more accurately to improve the numerical stability. We explain how this strategy can be applied to different compositions within a...
Go to contribution page
Choose timezone
Your profile timezone: