9 citations · 9 across the 4 of their papers we have counts for
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stat.CO2026
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…
stat.ML2026
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…
cs.LG2026
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…