165 citations · 318 across the 13 of their papers we have counts for
8 papers · 1 filter
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms
Trent Kyono, Yao Zhang, Alexis Bellot +1
Missing data is an important problem in machine learning practice. Starting from the premise that imputation methods should preserve the causal structure of the data, we develop a…
Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects
Yao Zhang, Jeroen Berrevoets, Mihaela van der Schaar
Conditional average treatment effects (CATEs) allow us to understand the effect heterogeneity across a large population of individuals. However, typical CATE learners assume all co…
Learning outside the Black-Box: The pursuit of interpretable models
Jonathan Crabbé, Yao Zhang, William Zame +1
Machine Learning has proved its ability to produce accurate models but the deployment of these models outside the machine learning community has been hindered by the difficulties o…
CASTLE: Regularization via Auxiliary Causal Graph Discovery
Trent Kyono, Yao Zhang, Mihaela van der Schaar
Regularization improves generalization of supervised models to out-of-sample data. Prior works have shown that prediction in the causal direction (effect from cause) results in low…
AutoCP: Automated Pipelines for Accurate Prediction Intervals
Yao Zhang, William Zame, Mihaela van der Schaar
Successful application of machine learning models to real-world prediction problems, e.g. financial forecasting and personalized medicine, has proved to be challenging, because suc…
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning
Yao Zhang, Daniel Jarrett, Mihaela van der Schaar
An essential problem in automated machine learning (AutoML) is that of model selection. A unique challenge in the sequential setting is the fact that the optimal model itself may v…
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning
Yao Zhang, Alpha A. Lee
Predicting bioactivity and physical properties of small molecules is a central challenge in drug discovery. Deep learning is becoming the method of choice but studies to date focus…
Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning
Yao Zhang, Andrew M. Saxe, Madhu S. Advani +1
Finding parameters that minimise a loss function is at the core of many machine learning methods. The Stochastic Gradient Descent algorithm is widely used and delivers state of the…