Bowen Jin, Hansi Zeng, Zhenrui Yue, Jinsung Yoon, Sercan Arik, Dong Wang, Hamed Zamani, Jiawei Han, “Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning,” Conference on Language Modeling (COLM), 2025. [Paper link][Github link]
Sungwon Han, Seungeon Lee, Meeyoung Cha, Sercan O Arik, Jinsung Yoon, “LLM-Guided Self-Supervised Tabular Learning With Task-Specific Pre-text Tasks,” Transactions on Machine Learning Research (TMLR), 2025. [Paper link]
Dongyoung Kim, Jinsung Yoon, Jinwoo Shin, Jaehyung Kim, “Debiasing Online Preference Learning via Preference Feature Preservation,” Annual Meeting of the Association for Computational Linguistics (ACL) - Findings, 2025.
Jinsung Yoon, Sercan O Arik, “Embedding-Converter: A Unified Framework for Cross-Model Embedding Transformation,” Annual Meeting of the Association for Computational Linguistics (ACL) - Main, 2025.
Bowen Jin, Jinsung Yoon, Zhen Qin, Ziqi Wang, Wei Xiong, Yu Meng, Jiawei Han, Sercan O Arik, “LLM Alignment as Retriever Optimization: An Information Retrieval Perspective,” International Conference on MachineLearning (ICML), 2025. [Paper link]
Sungwon Han, Seungeon Lee, Meeyoung Cha, Sercan O Arik, Jinsung Yoon, “Retrieval Augmented Time Series Forecasting,” International Conference on Machine Learning (ICML), 2025. [Paper link]
Bowen Jin, Jinsung Yoon, Jiawei Han, Sercan O Arik, “Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG,” International Conference on Learning Representations (ICLR), 2025. [Paper link]
Hongjin Su, Ruoxi Sun, Jinsung Yoon, Pengcheng Yin, Tao Yu, Sercan O Arik, “Learn-by-interact: A Data-Centric Framework For Self-Adaptive Agents in Realistic Environments,” International Conference on Learning Representations (ICLR), 2025. [Paper link]
Hongjin Su, Howard Yen, Mengzhou Xia, Weijia Shi, Niklas Muennighoff, Han-yu Wang, Liu Haisu, Quan Shi, Zachary S Siegel, Michael Tang, Ruoxi Sun, Jinsung Yoon, Sercan O Arik, Danqi Chen, Tao Yu, “BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval,” International Conference on Learning Representations (ICLR) - Spotlight, 2025. [Paper link]
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]
Jinsung Yoon, Yanfei Chen, Sercan O Arik, Tomas Pfister, “Search-Adaptor: Embedding Customization for Information Retrieval,” Annual Meeting of the Association for Computational Linguistics (ACL) - Main, 2024. [Paper link]
Jinsung Yoon, Rajarishi Sinha, Sercan O Arik, Tomas Pfister, “Matryoshka-Adaptor: Unsupervised and Supervised Tuning for Smaller Embedding Dimensions,” Empirical Methods in Natural Language Processing (EMNLP) - Main, 2024.
Yanfei Chen, Jinsung Yoon, Devendra Singh Sachan, Qingze Wang, Vincent Cohen-Addad, Mohammadhossein Bateni, Chen-Yu Lee, Tomas Pfister, “Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrieval,” Empirical Methods in Natural Language Processing (EMNLP) - Findings, 2024.
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) - Findings, 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
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
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
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
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
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]
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]