ML Conferences / Journals
2024
2024
- Zachary Izzo, Jinsung Yoon, Sercan O Arik, James Zou, “Provable Membership Inference Privacy,” Transactions on Machine Learning Research (TMLR), 2024. [Paper link]
- Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan O Arik, Somesh Jha, Tomas Pfister, “ASPEST: Bridging the Gap Between Active Learning and Selective Prediction,” Transactions on Machine Learning Research (TMLR), 2024. [Paper link]
2023
2023
- Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan O Arik, Tomas Pfister, Somesh Jha, “Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs,” Empirical Methods in Natural Language Processing (EMNLP), 2023. [Paper link]
- Yunhao Ge, Sercan O Arik, Jinsung Yoon, Ao Xu, Laurent Itti, Tomas Pfister, “Invariant Structure Learning for Better Generalization and Causal Explainability,” Transactions on Machine Learning Research (TMLR), 2023. [Paper link]
- Jinsung Yoon, Michel Mizrahi, Nahid Farhady Ghalaty, Thomas Jarvinen, Ashwin S. Ravi, Peter Brune, Fanyu Kong, Dave Anderson, George Lee, Arie Meir, Farhana Bandukwala, Elli Kanal, Sercan O Arik, Tomas Pfister, “EHR-Safe: Generating High-fidelity and Privacy-preserving Synthetic Electronic Health Records,” npj Digital Medicine, 2023. [Paper link]
- Eunbyeol Cho, Min Jae Lee, Kyunghoon Hur, Jiyoun Kim, Jinsung Yoon, Edward Choi, “Rediscovery of CNN's Versatility for Text-based Encoding of Raw Electronic Health Records,” Conference on Health, Inference, and Learning (CHIL), 2023. [Paper link] - Oral Presentation
- Aya Abdelsalam Ismail, Sercan O Arik, Jinsung Yoon, Ankur Taly, Soheil Feizi, Tomas Pfister, “Interpretable Mixture of Experts,” Transactions on Machine Learning Research (TMLR), 2023. [Paper link]
- Kihyuk Sohn, Jinsung Yoon, Chun-Liang Li, Chen-Yu Lee, Tomas Pfister, “Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types,” Winter Conference on Applications of Computer Vision (WACV), 2023. [Paper link]
- Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan O Arik, Tomas Pfister, “SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch,” Transactions on Machine Learning Research (TMLR), 2023. [Paper link] - Received Featured Certification
2022
2022
- Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan O Arik, Chen-Yu Lee, Tomas Pfister, “Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection,” Transactions on Machine Learning Research (TMLR), 2022. [Paper link]
- Jinsung Yoon, Sercan O Arik, Tomas Pfister, “LIMIS: Locally Interpretable Modeling using Instance-wise Subsampling,” Transactions on Machine Learning Research (TMLR), 2022. [Paper link]
2021
2021
- Jinsung Yoon, Daniel Jarrett, Ioana Bica, Zhaozhi Qian, Ari Ercole, Mihaela van der Schaar, “Clairvoyance: A Pipeline Toolkit for Medical Time Series,” International Conference on Learning Representations (ICLR), 2021. [Paper link]
- Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon, Minho Jin, and Tomas Pfister, “Learning and Evaluating Representations for Deep One-Class Classification,” International Conference on Learning Representations (ICLR), 2021. [Paper link] [Google AI Blog]
- Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, and Tomas Pfister, “CutPaste: Self-Supervised Learning for Anomaly Detection and Localization,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021. [paper link] [Google AI Blog]
- Sungyong Seo, Sercan O Arik, Jinsung Yoon, Xiang Zhang and Tomas Pfister, “Controlling Neural Networks with Rule Representations,” Neural Information Processing Systems (NeurIPS), 2021. [paper link]
2020
2020
- Jinsung Yoon, Yao Zhang, James Jordon, Mihaela van der Schaar, “VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain,” Neural Information Processing Systems (NeurIPS), 2020. [Paper link] [Code link]
- Sercan O. Arik, Chun-Liang Li, Jinsung Yoon, Rajarishi Sinha, Arkady Epshteyn, Long T. ¨ Le, Vikas Menon, Shashank Singh, Leyou Zhang, Nate Yoder, Martin Nikoltchev, Yash Sonthalia, Hootan Nakhost, Elli Kanal, and Tomas Pfister, “Interpretable Sequence Learning for COVID-19 Forecasting,” Neural Information Processing Systems (NeurIPS), 2020. [Paper link] [Blog link] - Selected as spotlight presentation
- Jinsung Yoon, Sercan O. Arik, Tomas Pfister, “Data Valuation using Reinforcement Learning,” International Conference on Machine Learning (ICML), 2020. [Paper link] [Code link] [Google AI Blog]
2019
2019
- Jinsung Yoon, Daniel Jarrett, Mihaela van der Schaar, “Time-series Generative Adversarial Networks,” Neural Information Processing Systems (NeurIPS), 2019. [Paper link] [Code link]
- James Jordon, Jinsung Yoon, M. van der Schaar, “Differentially Private Bagging: Improved Utility and Cheaper Privacy than Subsample-and-Aggregate,” Neural Information Processing Systems (NeurIPS), 2019. [Paper link] [Code link]
- Jinsung Yoon, James Jordon, Mihaela van der Schaar, “INVASE: Instance-wise Variable Selection using Neural Networks,” International Conference on Learning Representations (ICLR), 2019. [Paper link] [Code link]
- Jinsung Yoon, James Jordon, Mihaela van der Schaar, “PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees,” International Conference on Learning Representations (ICLR), 2019. [Paper link]
- James Jordon, Jinsung Yoon, Mihaela van der Schaar, “KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks,” International Conference on Learning Representations (ICLR), 2019. [Paper link] – Selected as oral presentation
2018
2018
- Jinsung Yoon, James Jordon, Mihaela van der Schaar, “GAIN: Missing Data Imputation using Generative Adversarial Nets,” International Conference on Machine Learning (ICML), 2018. [Paper link] [Code link]
- Jinsung Yoon, James Jordon, Mihaela van der Schaar, “RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks,” International Conference on Machine Learning (ICML), 2018. [Paper link]
- Jinsung Yoon, James Jordon, Mihaela van der Schaar, “GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets,” International Conference on Learning Representations (ICLR), 2018. [Paper link] [Code link]
- Jinsung Yoon, William R. Zame, Mihaela van der Schaar, “Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks,” International Conference on Learning Representations (ICLR), 2018. [Paper link]
- Changhee Lee, William R. Zame, Jinsung Yoon, Mihaela van der Schaar, “DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks,” Association for the Advancement of Artificial Intelligence (AAAI), 2018. [Paper link] [Code link]
2017 & Earlier
2017 & Earlier
- Jinsung Yoon, Ahmed M. Alaa, Martin Cadeiras, Mihaela van der Schaar, “Personalized Donor-Recipient Matching for Organ Transplantation,” Association for the Advancement of Artificial Intelligence (AAAI), 2017. [Paper link]
- Jinsung Yoon, Ahmed M. Alaa, Scott Hu, Mihaela van der Schaar, “ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit Admission,” International Conference on Machine Learning (ICML), 2016. [Paper link]