3 papers
cs.LG2026
On the Convergence of Multicalibration Gradient Boosting
Daniel Haimovich, Fridolin Linder, Lorenzo Perini +2
Multicalibration gradient boosting has recently emerged as a scalable method that empirically produces approximately multicalibrated predictors and has been deployed at web scale.…
cs.LG2026
MCGrad: Multicalibration at Web Scale
Niek Tax, Lorenzo Perini, Fridolin Linder +5
We propose MCGrad, a novel and scalable multicalibration algorithm. Multicalibration - calibration in subgroups of the data - is an important property for the performance of machin…
cs.LG2024
On the Convergence of Loss and Uncertainty-based Active Learning Algorithms
Daniel Haimovich, Dima Karamshuk, Fridolin Linder +2
We investigate the convergence rates and data sample sizes required for training a machine learning model using a stochastic gradient descent (SGD) algorithm, where data points are…