
Learning outside the BlackBox: The pursuit of interpretable models
Machine Learning has proved its ability to produce accurate models but t...
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CASTLE: Regularization via Auxiliary Causal Graph Discovery
Regularization improves generalization of supervised models to outofsa...
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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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CPAS: the UK's National Machine Learningbased Hospital Capacity Planning System for COVID19
The coronavirus disease 2019 (COVID19) global pandemic poses the threat...
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HideandSeek Privacy Challenge
The clinical timeseries setting poses a unique combination of challenge...
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Generalization and Invariances in the Presence of Unobserved Confounding
The ability to extrapolate, or generalize, from observed to new related ...
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Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift
Modern neural networks have proven to be powerful function approximators...
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Strictly Batch Imitation Learning by Energybased Distribution Matching
Consider learning a policy purely on the basis of demonstrated behavior—...
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Inverse Active Sensing: Modeling and Understanding Timely DecisionMaking
Evidencebased decisionmaking entails collecting (costly) observations ...
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AutoNCP: Automated pipelines for accurate confidence intervals
Successful application of machine learning models to realworld predicti...
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Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions
Recurrent neural networks (RNNs) are instrumental in modelling sequentia...
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Temporal Phenotyping using Deep Predictive Clustering of Disease Progression
Due to the wider availability of modern electronic health records, patie...
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Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification
Subgroup analysis of treatment effects plays an important role in applic...
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Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints
Phase I dosefinding trials are increasingly challenging as the relation...
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When and How to Lift the Lockdown? Global COVID19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes
The coronavirus disease 2019 (COVID19) global pandemic has led many cou...
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When to Lift the Lockdown? Global COVID19 Scenario Planning and Policy Effects using Compartmental Gaussian Processes
The coronavirus disease 2019 (COVID19) outbreak has led government offi...
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A primer on coupled stateswitching models for multiple interacting time series
Stateswitching models such as hidden Markov models or Markovswitching ...
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Estimating the Effects of Continuousvalued Interventions using Generative Adversarial Networks
While much attention has been given to the problem of estimating the eff...
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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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TargetEmbedding Autoencoders for Supervised Representation Learning
Autoencoderbased learning has emerged as a staple for disciplining repr...
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Learning Overlapping Representations for the Estimation of Individualized Treatment Effects
The choice of making an intervention depends on its potential benefit or...
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Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning
An essential problem in automated machine learning (AutoML) is that of m...
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Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes
Comorbid diseases cooccur and progress via complex temporal patterns th...
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Contextual Constrained Learning for DoseFinding Clinical Trials
Clinical trials in the medical domain are constrained by budgets. The nu...
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A Bayesian Approach to Modelling Longitudinal Data in Electronic Health Records
Analyzing electronic health records (EHR) poses significant challenges b...
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Improving Model Robustness Using Causal Knowledge
For decades, researchers in fields, such as the natural and social scien...
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A Robust TwoSample Test for Time Series data
We develop a general framework for hypothesis testing with time series d...
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Conditional Independence Testing using Generative Adversarial Networks
We consider the hypothesis testing problem of detecting conditional depe...
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ASAC: Active Sensing using ActorCritic models
Deciding what and when to observe is critical when making observations i...
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Lifelong Bayesian Optimization
Automatic Machine Learning (AutoML) systems tackle the problem of autom...
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Machine Learning in the Air
Thanks to the recent advances in processing speed and data acquisition a...
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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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Distributed Task Management in CyberPhysical Systems: How to Cooperate under Uncertainty?
We consider the problem of task allocation in a network of cyberphysica...
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What is Interpretable? Using Machine Learning to Design Interpretable DecisionSupport Systems
Recent efforts in Machine Learning (ML) interpretability have focused on...
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MATCHNet: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks
Accurate prediction of disease trajectories is critical for early identi...
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Feature Selection for Survival Analysis with Competing Risks using Deep Learning
Deep learning models for survival analysis have gained significant atten...
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Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning
Estimating the individual treatment effect (ITE) from observational data...
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RiskStratify: Confident Stratification Of Patients Based On Risk
A clinician desires to use a riskstratification method that achieves co...
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MAMMO: A Deep Learning Solution for Facilitating RadiologistMachine Collaboration in Breast Cancer Diagnosis
With an aging and growing population, the number of women requiring eith...
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Generalized Concordance for Competing Risks
Existing metrics in competing risks survival analysis such as concordanc...
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Forecasting Individualized Disease Trajectories using Interpretable Deep Learning
Disease progression models are instrumental in predicting individuallev...
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Sequential Patient Recruitment and Allocation for Adaptive Clinical Trials
Randomized Controlled Trials (RCTs) are the gold standard for comparing ...
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Siamese Survival Analysis with Competing Risks
Survival analysis in the presence of multiple possible adverse events, i...
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Forecasting Disease Trajectories in Alzheimer's Disease Using Deep Learning
Joint models for longitudinal and timetoevent data are commonly used i...
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Measuring the quality of Synthetic data for use in competitions
Machine learning has the potential to assist many communities in using t...
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Piecewise Approximations of Black Box Models for Model Interpretation
Machine Learning models have proved extremely successful for a wide vari...
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GAIN: Missing Data Imputation using Generative Adversarial Nets
We propose a novel method for imputing missing data by adapting the well...
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DiseaseAtlas: Navigating Disease Trajectories with Deep Learning
Joint models for longitudinal and timetoevent data are commonly used i...
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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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RadialGAN: Leveraging multiple datasets to improve targetspecific predictive models using Generative Adversarial Networks
Training complex machine learning models for prediction often requires a...
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