6 papers
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.…
Billion-Scale Graph Foundation Models
Maya Bechler-Speicher, Yoel Gottlieb, Andrey Isakov +5
Graph-structured data underpins many critical applications. While foundation models have transformed language and vision via large-scale pretraining and lightweight adaptation, ext…
Measuring multi-calibration
Ido Guy, Daniel Haimovich, Fridolin Linder +4
A suitable scalar metric can help measure multi-calibration, defined as follows. When the expected values of observed responses are equal to corresponding predicted probabilities,…
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…
Multicalibration Yields Better Matchings
Riccardo Colini Baldeschi, Simone Di Gregorio, Simone Fioravanti +9
Consider the problem of finding the best matching in a weighted graph where we only have access to predictions of the actual stochastic weights, based on an underlying context. If…
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…