Widening Access to Applied Machine Learning with TinyML

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.

BIG-bench Machine Learning

Estimation and Inference on Nonlinear and Heterogeneous Effects

no code implementations • 16 Mar 2017 • Marc Ratkovic, Dustin Tingley

Importantly, we introduce a robust approach to uncertainty estimates.

BIG-bench Machine Learning Causal Inference +1

Delving Deeper into MOOC Student Dropout Prediction

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.

regression

TopicCheck: Interactive Alignment for Assessing Topic Model Stability

no code implementations • HLT 2015 • Jason Chuang, Jeffrey Heer, Margaret E. Roberts, Brandon M. Stewart, Rebecca Weiss, Dustin Tingley, Justin Grimmer

Topic Models