← Authors

James Jordon

15 papers · 2018–2023 · 5 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (5) 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (20) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🐝 Cross-Pollinator (12) 🌍 Conference Polyglot (5) 🀝 Dynamic Duo (11) πŸ’Ž Century Club (15) πŸš€ Conference Pioneer ⚑ Prolific Year (5) ❓ The Questioner

Conferences

ICLR (5) NIPS (4) ICML (3) AISTATS (2) MLHC (1)

Top co-authors

Mihaela van der Schaar (11) Jinsung Yoon (9) Mihaela Schaar (4) Jeroen Berrevoets (3) Ioana Bica (3) alexander gimson (1) Zhaozhi Qian (1) Trent Kyono (1) Fergus Imrie (1) Ahmed M Alaa (1)

Research topics

Differential Privacy (1)

Keywords

causal inference (3) generative adversarial network (3) organ transplantation (2) treatment effect (2) domain adaptation (1) cluster analysis (1) semi-supervised learning (1) data augmentation (1) transfer learning (1) adversarial training (1) resource allocation (1) survival analysis (1) target domain (1) active sensing (1) self-supervised learning (1) ensemble method (1) missing data imputation (1) safety constraint (1) counterfactual reasoning (1) sequential decision making (1)

Papers

To Impute or not to Impute? Missing Data in Treatment Effect Estimation AISTATS 2023
Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis ICML 2021
OrganITE: Optimal transplant donor organ offering using an individual treatment effect NIPS 2020
VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain NIPS 2020
Contextual Constrained Learning for Dose-Finding Clinical Trials AISTATS 2020
Estimating counterfactual treatment outcomes over time through adversarially balanced representations ICLR 2020
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks NIPS 2020
ASAC: Active Sensing using Actor-Critic models MLHC 2019
KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks ICLR 2019
PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees ICLR 2019
INVASE: Instance-wise Variable Selection using Neural Networks ICLR 2019
Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate NIPS 2019
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks ICML 2018
GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets ICLR 2018
GAIN: Missing Data Imputation using Generative Adversarial Nets ICML 2018