5 papers
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…
Online Learning with Sublinear Best-Action Queries
Matteo Russo, Andrea Celli, Riccardo Colini Baldeschi +5
In online learning, a decision maker repeatedly selects one of a set of actions, with the goal of minimizing the overall loss incurred. Following the recent line of research on alg…
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…
Bandits for Online Calibration: An Application to Content Moderation on Social Media Platforms
Vashist Avadhanula, Omar Abdul Baki, Hamsa Bastani +17
We describe the current content moderation strategy employed by Meta to remove policy-violating content from its platforms. Meta relies on both handcrafted and learned risk models…
CUT: Controllable Unsupervised Text Simplification
Oleg Kariuk, Dima Karamshuk
In this paper, we focus on the challenge of learning controllable text simplifications in unsupervised settings. While this problem has been previously discussed for supervised lea…