165 citations · 318 across the 13 of their papers we have counts for
13 papers
Graph Attention Transformer Network for Multi-Label Image Classification
Jin Yuan, Shikai Chen, Yao Zhang +4
Multi-label classification aims to recognize multiple objects or attributes from images. However, it is challenging to learn from proper label graphs to effectively characterize su…
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…
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…
Lifelong Bayesian Optimization
Yao Zhang, James Jordon, Ahmed M. Alaa +1
Automatic Machine Learning (Auto-ML) systems tackle the problem of automating the design of prediction models or pipelines for data science. In this paper, we present Lifelong Baye…
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…
Geometry of energy landscapes and the optimizability of deep neural networks
Simon Becker, Yao Zhang, Alpha A. Lee
Deep neural networks are workhorse models in machine learning with multiple layers of non-linear functions composed in series. Their loss function is highly non-convex, yet empiric…
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…