265 citations · 1.6k across the 43 of their papers we have counts for
3 papers · 1 filter
Learning to Induce Causal Structure
Nan Rosemary Ke, Silvia Chiappa, Jane Wang +7
The fundamental challenge in causal induction is to infer the underlying graph structure given observational and/or interventional data. Most existing causal induction algorithms o…
A case for new neural network smoothness constraints
Mihaela Rosca, Theophane Weber, Arthur Gretton +1
How sensitive should machine learning models be to input changes? We tackle the question of model smoothness and show that it is a useful inductive bias which aids generalization,…
Automated Variational Inference in Probabilistic Programming
David Wingate, Theophane Weber
We present a new algorithm for approximate inference in probabilistic programs, based on a stochastic gradient for variational programs. This method is efficient without restrictio…