25 citations · 83 across the 18 of their papers we have counts for
19 papers
Asset Trading in Continuous Time: A Cautionary Tale
William R. Zame
The continuous time model of dynamic asset trading is the central model of modern finance. Because trading cannot in fact take place at every moment of time, it would seem desirabl…
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…
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…
Adaptive Clinical Trials: Exploiting Sequential Patient Recruitment and Allocation
Onur Atan, William R. Zame, Mihaela van der Schaar
Randomized Controlled Trials (RCTs) are the gold standard for comparing the effectiveness of a new treatment to the current one (the control). Most RCTs allocate the patients to th…
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…
Learning Optimal Policies from Observational Data
Onur Atan, William R. Zame, M van der Schaar
Choosing optimal (or at least better) policies is an important problem in domains from medicine to education to finance and many others. One approach to this problem is through con…
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…
ToPs: Ensemble Learning with Trees of Predictors
Jinsung Yoon, William R. Zame, Mihaela van der Schaar
We present a new approach to ensemble learning. Our approach constructs a tree of subsets of the feature space and associates a predictor (predictive model) - determined by trainin…
Personalized Survival Predictions for Cardiac Transplantation via Trees of Predictors
J. Yoon, W. R. Zame, A. Banerjee +3
Given the limited pool of donor organs, accurate predictions of survival on the wait list and post transplantation are crucial for cardiac transplantation decisions and policy. How…
Constructing Effective Personalized Policies Using Counterfactual Inference from Biased Data Sets with Many Features
Onur Atan, William R. Zame, Qiaojun Feng +1
This paper proposes a novel approach for constructing effective personalized policies when the observed data lacks counter-factual information, is biased and possesses many feature…
A Theory of Individualism, Collectivism and Economic Outcomes
Kartik Ahuja, Mihaela van der Schaar, William R. Zame
This paper presents a dynamic model to study the impact on the economic outcomes in different societies during the Malthusian Era of individualism (time spent working alone) and co…
Designing Efficient Resource Sharing For Impatient Players Using Limited Monitoring
Mihaela van der Schaar, Yuanzhang Xiao, William Zame
The problem of efficient sharing of a resource is nearly ubiquitous. Except for pure public goods, each agent's use creates a negative externality; often the negative externality i…
Designing Information Revelation and Intervention with an Application to Flow Control
Luca Canzian, Yuanzhang Xiao, William Zame +2
There are many familiar situations in which a manager seeks to design a system in which users share a resource, but outcomes depend on the information held and actions taken by use…
Designing Practical Distributed Exchange for Online Communities
Jie Xu, Mihaela van der Schaar, William Zame
In many online systems, individuals provide services for each other; the recipient of the service obtains a benefit but the provider of the service incurs a cost. If benefit exceed…
Technology Choices and Pricing Policies in Public and Private Wireless Networks
Yuanzhang Xiao, William R. Zame, Mihaela van der Schaar
This paper studies the provision of a wireless network by a monopolistic provider who may be either benevolent (seeking to maximize social welfare) or selfish (seeking to maximize…