9 citations · 9 across the 4 of their papers we have counts for
6 papers
How to Train Your Intercept: A Comparison of Intercept Update Strategies in Coordinate Descent
Johan Larsson, Frederik Fabricius-Bjerre
Coordinate descent solvers for regularized generalized linear models (GLMs) differ in how they update the intercept. This seemingly routine choice can substantially affect converge…
Feature Normalization under Finite Training Budgets
Johan Larsson
Standardizing a rare feature can help stochastic gradient descent learn its effect within a finite budget, but it also changes the noise cost of learning. We characterize the lowes…
Basin: Efficient and Extensible Numerical Optimization in Rust
Johan Larsson
Basin is a numerical optimization library for the Rust programming language. Numerical optimization is the task of finding the inputs that minimize a function, and it is a fundamen…
Efficient Solvers for SLOPE in R, Python, Julia, and C++
Johan Larsson, Malgorzata Bogdan, Krystyna Grzesiak +2
We present a suite of packages in R, Python, Julia, and C++ that efficiently solve the Sorted L-One Penalized Estimation (SLOPE) problem. The packages feature a highly efficient hy…
The Choice of Normalization Influences Shrinkage in Regularized Regression
Johan Larsson, Jonas Wallin
Regularized models are often sensitive to the scales of the features in the data and it has therefore become standard practice to normalize (center and scale) the features before f…
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
Thomas Moreau, Mathurin Massias, Alexandre Gramfort +18
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice.…