165 citations · 318 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
Woodpecker-DL: Accelerating Deep Neural Networks via Hardware-Aware Multifaceted Optimizations
Yongchao Liu, Yue Jin, Yong Chen +4
Accelerating deep model training and inference is crucial in practice. Existing deep learning frameworks usually concentrate on optimizing training speed and pay fewer attentions t…
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…
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification
Hyun-Suk Lee, Yao Zhang, William Zame +3
Subgroup analysis of treatment effects plays an important role in applications from medicine to public policy to recommender systems. It allows physicians (for example) to identify…
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…