Scalable Prediction of Information Cascades over Arbitrary Time Horizons. - Daniel Haimovich; Dima Karamshuk; Thomas J. Leeper; Evgeniy Riabenko; Milan Vojnovic (CoRR, 2020)

Weights and Methodology Brief for the COVID-19 Symptom Survey by University of Maryland and Carnegie Mellon University, in Partnership with Facebook. - Neta Barkay; Curtiss Cobb; Roee Eilat; Tal Galili; Daniel Haimovich; Sarah LaRocca; Katherine Morris; Tal Sarig (CoRR, 2020)

Popularity Prediction for Social Media over Arbitrary Time Horizons. - Daniel Haimovich; Dmytro Karamshuk; Thomas J. Leeper; Evgeniy Riabenko; Milan Vojnovic (Proc. VLDB Endow., 2021)

Bandits for Online Calibration: An Application to Content Moderation on Social Media Platforms. - Vashist Avadhanula; Omar Abdul Baki; Hamsa Bastani; Osbert Bastani; Caner Gocmen; Daniel Haimovich; Darren Hwang; Dima Karamshuk; Thomas J. Leeper; Jiayuan Ma; Gregory Macnamara; Jake Mullett; et al. (CoRR, 2022)

Online Learning with Sublinear Best-Action Queries. - Matteo Russo 002; Andrea Celli; Riccardo Colini-Baldeschi; Federico Fusco; Daniel Haimovich; Dima Karamshuk; Stefano Leonardi 001; Niek Tax (NeurIPS, 2024)

On the Convergence of Loss and Uncertainty-based Active Learning Algorithms. - Daniel Haimovich; Dima Karamshuk; Fridolin Linder; Niek Tax; Milan Vojnovic (NeurIPS, 2024)

Measuring multi-calibration. - Ido Guy; Daniel Haimovich; Fridolin Linder; Nastaran Okati; Lorenzo Perini; Niek Tax; Mark Tygert (CoRR, 2025)