Search Results for author: William Zame

Found 6 papers, 3 papers with code

SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups

no code implementations26 Jan 2021 Hyun-Suk Lee, Cong Shen, William Zame, Jang-Won Lee, Mihaela van der Schaar

Phase I clinical trials are designed to test the safety (non-toxicity) of drugs and find the maximum tolerated dose (MTD).

Learning outside the Black-Box: The pursuit of interpretable models

1 code implementation NeurIPS 2020 Jonathan Crabbé, Yao Zhang, William Zame, Mihaela van der Schaar

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 of interpreting these models.

AutoCP: Automated Pipelines for Accurate Prediction Intervals

no code implementations24 Jun 2020 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 such settings require limiting and quantifying the uncertainty in the model predictions, i. e. providing valid and accurate prediction intervals.

AutoML Prediction Intervals +1

Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification

1 code implementation NeurIPS 2020 Hyun-Suk Lee, Yao Zhang, William Zame, Cong Shen, Jang-Won Lee, Mihaela van der Schaar

Most of the current methods of subgroup analysis begin with a particular algorithm for estimating individualized treatment effects (ITE) and identify subgroups by maximizing the difference across subgroups of the average treatment effect in each subgroup.

Recommendation Systems

Optimal Piecewise Local-Linear Approximations

1 code implementation27 Jun 2018 Kartik Ahuja, William Zame, Mihaela van der Schaar

Piecewise local-linear models provide a natural way to extend local-linear models to explain the global behavior of the model.

DPSCREEN: Dynamic Personalized Screening

no code implementations NeurIPS 2017 Kartik Ahuja, William Zame, Mihaela van der Schaar

However, there has been limited work to address the personalized screening for these different diseases.

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