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11/12/2022
TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data
Personal data collected at scale promises to improve decision-making and...
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05/06/2022
Synthetic Data – what, why and how?
This explainer document aims to provide an overview of the current state...
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02/04/2022
To Impute or not to Impute? – Missing Data in Treatment Effect Estimation
Missing data is a systemic problem in practical scenarios that causes no...
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12/08/2020
Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods
Many ground-breaking advancements in machine learning can be attributed ...
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07/23/2020
Hide-and-Seek Privacy Challenge
The clinical time-series setting poses a unique combination of challenge...
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02/27/2020
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks
While much attention has been given to the problem of estimating the eff...
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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
Contextual Constrained Learning for Dose-Finding Clinical Trials
Clinical trials in the medical domain are constrained by budgets. The nu...
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06/16/2019
ASAC: Active Sensing using Actor-Critic models
Deciding what and when to observe is critical when making observations i...
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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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06/29/2018
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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06/07/2018
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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02/18/2018