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06/16/2023
Aligning Synthetic Medical Images with Clinical Knowledge using Human Feedback
Generative models capable of capturing nuanced clinical features in medi...
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05/08/2023
Large-Scale Study of Temporal Shift in Health Insurance Claims
Most machine learning models for predicting clinical outcomes are develo...
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04/04/2023
Conformalized Unconditional Quantile Regression
We develop a predictive inference procedure that combines conformal pred...
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02/17/2021
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models
Devising domain- and model-agnostic evaluation metrics for generative mo...
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01/28/2021
Learning Matching Representations for Individualized Organ Transplantation Allocation
Organ transplantation is often the last resort for treating end-stage il...
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08/14/2020
Semiparametric Estimation and Inference on Structural Target Functions using Machine Learning and Influence Functions
We aim to construct a class of learning algorithms that are of practical...
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07/27/2020
CPAS: the UK's National Machine Learning-based Hospital Capacity Planning System for COVID-19
The coronavirus disease 2019 (COVID-19) global pandemic poses the threat...
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06/26/2020
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift
Modern neural networks have proven to be powerful function approximators...
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06/20/2020
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions
Recurrent neural networks (RNNs) are instrumental in modelling sequentia...
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05/13/2020
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes
The coronavirus disease 2019 (COVID-19) global pandemic has led many cou...
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05/13/2020
When to Lift the Lockdown? Global COVID-19 Scenario Planning and Policy Effects using Compartmental Gaussian Processes
The coronavirus disease 2019 (COVID-19) outbreak has led government offi...
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02/10/2020
Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations
Identifying when to give treatments to patients and how to select among ...
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01/08/2020
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes
Comorbid diseases co-occur and progress via complex temporal patterns th...
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05/29/2019
Lifelong Bayesian Optimization
Automatic Machine Learning (Auto-ML) systems tackle the problem of autom...
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02/01/2019
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders
The estimation of treatment effects is a pervasive problem in medicine. ...
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10/24/2018
Forecasting Individualized Disease Trajectories using Interpretable Deep Learning
Disease progression models are instrumental in predicting individual-lev...
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02/20/2018
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning
Clinical prognostic models derived from largescale healthcare data can i...
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12/24/2017
Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms
We investigate the problem of estimating the causal effect of a treatmen...
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06/19/2017
Deep Counterfactual Networks with Propensity-Dropout
We propose a novel approach for inferring the individualized causal effe...
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12/18/2016
A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference
Modeling continuous-time physiological processes that manifest a patient...
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11/16/2016
A Semi-Markov Switching Linear Gaussian Model for Censored Physiological Data
Critically ill patients in regular wards are vulnerable to unanticipated...
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10/27/2016
Personalized Risk Scoring for Critical Care Prognosis using Mixtures of Gaussian Processes
Objective: In this paper, we develop a personalized real-time risk scori...
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10/24/2016
Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition
We develop a Bayesian model for decision-making under time pressure with...
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05/03/2016