Search Results for author: James Jordon

Found 18 papers, 14 papers with code

Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods

no code implementations8 Dec 2020 James Jordon, Alan Wilson, Mihaela van der Schaar

Many ground-breaking advancements in machine learning can be attributed to the availability of a large volume of rich data.

Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks

1 code implementation NeurIPS 2020 Ioana Bica, James Jordon, Mihaela van der Schaar

While much attention has been given to the problem of estimating the effect of discrete interventions from observational data, relatively little work has been done in the setting of continuous-valued interventions, such as treatments associated with a dosage parameter.

Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations

2 code implementations ICLR 2020 Ioana Bica, Ahmed M. Alaa, James Jordon, Mihaela van der Schaar

Identifying when to give treatments to patients and how to select among multiple treatments over time are important medical problems with a few existing solutions.

Contextual Constrained Learning for Dose-Finding Clinical Trials

1 code implementation8 Jan 2020 Hyun-Suk Lee, Cong Shen, James Jordon, Mihaela van der Schaar

In addition, patient recruitment can be difficult by the fact that clinical trials do not aim to provide a benefit to any given patient in the trial.

Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate

1 code implementation NeurIPS 2019 James Jordon, Jinsung Yoon, Mihaela van der Schaar

The second benefit is that, through analysis that we provide inthe paper, we can derive tighter differential privacy guarantees when several queriesare made to this mechanism.

ASAC: Active Sensing using Actor-Critic models

1 code implementation16 Jun 2019 Jinsung Yoon, James Jordon, Mihaela van der Schaar

The predictor network uses the observations selected by the selector network to predict a label, providing feedback to the selector network (well-selected variables should be predictive of the label).

Lifelong Bayesian Optimization

no code implementations29 May 2019 Yao Zhang, James Jordon, Ahmed M. Alaa, Mihaela van der Schaar

In this paper, we present Lifelong Bayesian Optimization (LBO), an online, multitask Bayesian optimization (BO) algorithm designed to solve the problem of model selection for datasets arriving and evolving over time.

Model Selection

PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees

1 code implementation ICLR 2019 Jinsung Yoon, James Jordon, Mihaela van der Schaar

Machine learning has the potential to assist many communities in using the large datasets that are becoming more and more available.

Synthetic Data Generation

INVASE: Instance-wise Variable Selection using Neural Networks

1 code implementation ICLR 2019 Jinsung Yoon, James Jordon, Mihaela van der Schaar

The advent of big data brings with it data with more and more dimensions and thus a growing need to be able to efficiently select which features to use for a variety of problems.

Variable Selection

KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks

1 code implementation ICLR 2019 James Jordon, Jinsung Yoon, Mihaela van der Schaar

We demonstrate the capability of our model to perform feature selection, showing that it performs as well as the originally proposed knockoff generation model in the Gaussian setting and that it outperforms the original model in non-Gaussian settings, including on a real-world dataset.

Measuring the quality of Synthetic data for use in competitions

1 code implementation29 Jun 2018 James Jordon, Jinsung Yoon, Mihaela van der Schaar

Machine learning has the potential to assist many communities in using the large datasets that are becoming more and more available.

RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks

1 code implementation ICML 2018 Jinsung Yoon, James Jordon, Mihaela van der Schaar

Training complex machine learning models for prediction often requires a large amount of data that is not always readily available.

GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets

1 code implementation ICLR 2018 Jinsung Yoon, James Jordon, Mihaela van der Schaar

Estimating individualized treatment effects (ITE) is a challenging task due to the need for an individual's potential outcomes to be learned from biased data and without having access to the counterfactuals.

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