4 papers · 1 filter
Stochastic Lanczos Quadrature for Computational Uncertainty in Linear Algebra
Hassan Fifen, Issa Karambal, Philipp Hennig
Applying a matrix function to a vector is a common operation in large-scale scientific computing, such as Bayesian inference. When the matrix is symmetric positive definite and the…
ReliableNet: A Chance-Constrained Approach to Trustworthy Classification in Deep Learning
Ange-Clément Akazan, Ineza Remy Mugenga, Abebe Geletu +2
A prediction that is both confident and wrong is a critical reliability failure because it can bypass abstention and human review precisely when the model is mistaken. Empirical ri…
A Range-Deflated Stochastic Lanczos Quadrature Method for Large-Scale Log-Determinant Estimation
Verlon Roel Mbingui, Antoine Tambue, Issa Karambal
Estimating the logarithm of the determinant of large sparse symmetric positive definite matrices is an important problem in numerical linear algebra, machine learning, Gaussian pro…
Novel technique based on Léja Points Approximation for Log-determinant Estimation of Large matrices
Verlon Roel Mbingui, Antoine Tambue, Issa Karambal
The computation of the Log-determinant of large, sparse, symmetric positive definite (SPD) matrices is essential in many scientific computational fields such as numerical linear al…