1 code implementation • 7 Jun 2021 • Vijay Janapa Reddi, Brian Plancher, Susan Kennedy, Laurence Moroney, Pete Warden, Anant Agarwal, Colby Banbury, Massimo Banzi, Matthew Bennett, Benjamin Brown, Sharad Chitlangia, Radhika Ghosal, Sarah Grafman, Rupert Jaeger, Srivatsan Krishnan, Maximilian Lam, Daniel Leiker, Cara Mann, Mark Mazumder, Dominic Pajak, Dhilan Ramaprasad, J. Evan Smith, Matthew Stewart, Dustin Tingley
Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation.
no code implementations • 16 Mar 2017 • Marc Ratkovic, Dustin Tingley
Importantly, we introduce a robust approach to uncertainty estimates.
no code implementations • 21 Feb 2017 • Jacob Whitehill, Kiran Mohan, Daniel Seaton, Yigal Rosen, Dustin Tingley
In order to obtain reliable accuracy estimates for automatic MOOC dropout predictors, it is important to train and test them in a manner consistent with how they will be used in practice.