Composing graphical models with neural networks for structured representations and fast inference. - Matthew J. Johnson 002; David Duvenaud; Alexander B. Wiltschko; Ryan P. Adams; Sandeep R. Datta (NIPS, 2016)

Tangent: Automatic Differentiation Using Source Code Transformation in Python. - Bart van Merriënboer; Alexander B. Wiltschko; Dan Moldovan (CoRR, 2017)

Winner's Curse? On Pace, Progress, and Empirical Rigor. - D. Sculley; Jasper Snoek; Alexander B. Wiltschko; Ali Rahimi (ICLR (Workshop), 2018)

Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming. - Bart van Merrienboer; Dan Moldovan; Alexander B. Wiltschko (NeurIPS, 2018)

AutoGraph: Imperative-style Coding with Graph-based Performance. - Dan Moldovan; James M. Decker; Fei Wang 046; Andrew A. Johnson 002; Brian K. Lee; Zachary Nado; D. Sculley; Tiark Rompf; Alexander B. Wiltschko (SysML, 2019)

Avoiding a Tragedy of the Commons in the Peer Review Process. - D. Sculley; Jasper Snoek; Alexander B. Wiltschko (CoRR, 2019)

Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules. - Benjamín Sánchez-Lengeling; Jennifer N. Wei; Brian K. Lee; Richard C. Gerkin; Alán Aspuru-Guzik; Alexander B. Wiltschko (CoRR, 2019)

Evaluating Attribution for Graph Neural Networks. - Benjamín Sánchez-Lengeling; Jennifer N. Wei; Brian K. Lee; Emily Reif; Peter Wang; Wesley Wei Qian; Kevin McCloskey; Lucy J. Colwell; Alexander B. Wiltschko (NeurIPS, 2020)