25 citations · 83 across the 18 of their papers we have counts for
7 papers · 1 filter
Inferring Lexicographically-Ordered Rewards from Preferences
Alihan Hüyük, William R. Zame, Mihaela van der Schaar
Modeling the preferences of agents over a set of alternatives is a principal concern in many areas. The dominant approach has been to find a single reward/utility function with the…
Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression
Zhaozhi Qian, William R. Zame, Lucas M. Fleuren +2
Modeling a system's temporal behaviour in reaction to external stimuli is a fundamental problem in many areas. Pure Machine Learning (ML) approaches often fail in the small sample…
SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups
Hyun-Suk Lee, Cong Shen, William Zame +2
Phase I clinical trials are designed to test the safety (non-toxicity) of drugs and find the maximum tolerated dose (MTD). This task becomes significantly more challenging when mul…
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…
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…
Optimal Piecewise Local-Linear Approximations
Kartik Ahuja, William Zame, Mihaela van der Schaar
Existing works on "black-box" model interpretation use local-linear approximations to explain the predictions made for each data instance in terms of the importance assigned to the…
Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks
Jinsung Yoon, William R. Zame, Mihaela van der Schaar
Missing data is a ubiquitous problem. It is especially challenging in medical settings because many streams of measurements are collected at different - and often irregular - times…