Adaptive Ensemble Learning with Confidence Bounds for Personalized Diagnosis. - Cem Tekin; Jinsung Yoon; Mihaela van der Schaar (AAAI Workshop: Expanding the Boundaries of Health Informatics Using AI, 2016)

ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit Admission. - Jinsung Yoon; Ahmed M. Alaa; Scott Hu; Mihaela van der Schaar (ICML, 2016)

Personalized Risk Scoring for Critical Care Patients using Mixtures of Gaussian Process Experts. - Ahmed M. Alaa; Jinsung Yoon; Scott Hu; Mihaela van der Schaar (CoRR, 2016)

A Semi-Markov Switching Linear Gaussian Model for Censored Physiological Data. - Ahmed M. Alaa; Jinsung Yoon; Scott Hu; Mihaela van der Schaar (CoRR, 2016)

Personalized Donor-Recipient Matching for Organ Transplantation. - Jinsung Yoon; Ahmed M. Alaa; Martin Cadeiras; Mihaela van der Schaar (AAAI, 2017)

Individualized Risk Prognosis for Critical Care Patients: A Multi-task Gaussian Process Model. - Ahmed M. Alaa; Jinsung Yoon; Scott Hu; Mihaela van der Schaar (CoRR, 2017)

Personalized Survival Predictions for Cardiac Transplantation via Trees of Predictors. - Jinsung Yoon; William R. Zame; Amitava Banerjee; Martin Cadeiras; Ahmed M. Alaa; Mihaela van der Schaar (CoRR, 2017)

Discovery and Clinical Decision Support for Personalized Healthcare. - Jinsung Yoon; Camelia Davtyan; Mihaela van der Schaar (IEEE J. Biomed. Health Informatics, 2017)

Adaptive Ensemble Learning With Confidence Bounds. - Cem Tekin; Jinsung Yoon; Mihaela van der Schaar (IEEE Trans. Signal Process., 2017)

DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks. - Changhee Lee; William R. Zame; Jinsung Yoon; Mihaela van der Schaar (AAAI, 2018)

GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets. - Jinsung Yoon; James Jordon; Mihaela van der Schaar (ICLR (Poster), 2018)

Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks. - Jinsung Yoon; William R. Zame; Mihaela van der Schaar (ICLR (Poster), 2018)

GAIN: Missing Data Imputation using Generative Adversarial Nets. - Jinsung Yoon; James Jordon; Mihaela van der Schaar (ICML, 2018)

RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks. - Jinsung Yoon; James Jordon; Mihaela van der Schaar (ICML, 2018)

Measuring the quality of Synthetic data for use in competitions. - James Jordon; Jinsung Yoon; Mihaela van der Schaar (CoRR, 2018)

Feature Selection for Survival Analysis with Competing Risks using Deep Learning. - Carl Rietschel; Jinsung Yoon; Mihaela van der Schaar (CoRR, 2018)

MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks. - Daniel Jarrett; Jinsung Yoon; Mihaela van der Schaar (CoRR, 2018)

Personalized Risk Scoring for Critical Care Prognosis Using Mixtures of Gaussian Processes. - Ahmed M. Alaa; Jinsung Yoon; Scott Hu; Mihaela van der Schaar (IEEE Trans. Biomed. Eng., 2018)

ToPs: Ensemble Learning With Trees of Predictors. - Jinsung Yoon; William R. Zame; Mihaela van der Schaar (IEEE Trans. Signal Process., 2018)

Design of a Dynamic Data-Driven System for Multispectral Video Processing. - Honglei Li 005; Yanzhou Liu 001; Kishan Sudusinghe; Jinsung Yoon; Erik Blasch; Mihaela van der Schaar; Shuvra S. Bhattacharyya (Handbook of Dynamic Data Driven Applications Systems, 2018)

KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks. - James Jordon; Jinsung Yoon; Mihaela van der Schaar (ICLR, 2019)

PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees. - James Jordon; Jinsung Yoon; Mihaela van der Schaar (ICLR (Poster), 2019)

INVASE: Instance-wise Variable Selection using Neural Networks. - Jinsung Yoon; James Jordon; Mihaela van der Schaar (ICLR (Poster), 2019)

ASAC: Active Sensing using Actor-Critic models. - Jinsung Yoon; James Jordon; Mihaela van der Schaar (MLHC, 2019)

Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate. - James Jordon; Jinsung Yoon; Mihaela van der Schaar (NeurIPS, 2019)

Time-series Generative Adversarial Networks. - Jinsung Yoon; Daniel Jarrett; Mihaela van der Schaar (NeurIPS, 2019)

RL-LIM: Reinforcement Learning-based Locally Interpretable Modeling. - Jinsung Yoon; Sercan Ömer Arik; Tomas Pfister (CoRR, 2019)

Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networks. - Jinsung Yoon; William R. Zame; Mihaela van der Schaar (IEEE Trans. Biomed. Eng., 2019)

Data Valuation using Reinforcement Learning. - Jinsung Yoon; Sercan Ömer Arik; Tomas Pfister (ICML, 2020)

Interpretable Sequence Learning for Covid-19 Forecasting. - Sercan Ömer Arik; Chun-Liang Li; Jinsung Yoon; Rajarishi Sinha; Arkady Epshteyn; Long T. Le; Vikas Menon; Shashank Singh 005; Leyou Zhang; Martin Nikoltchev; Yash Sonthalia; Hootan Nakhost; et al. (NeurIPS, 2020)

Hide-and-Seek Privacy Challenge: Synthetic Data Generation vs. Patient Re-identification. - James Jordon; Daniel Jarrett; Evgeny Saveliev; Jinsung Yoon; Paul W. G. Elbers; Patrick Thoral; Ari Ercole; Cheng Zhang 005; Danielle Belgrave; Mihaela van der Schaar (NeurIPS (Competition and Demos), 2020)

VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain. - Jinsung Yoon; Yao Zhang; James Jordon; Mihaela van der Schaar (NeurIPS, 2020)

Hide-and-Seek Privacy Challenge. - James Jordon; Daniel Jarrett; Jinsung Yoon; Tavian Barnes; Paul W. G. Elbers; Patrick Thoral; Ari Ercole; Cheng Zhang 005; Danielle Belgrave; Mihaela van der Schaar (CoRR, 2020)

Interpretable Sequence Learning for COVID-19 Forecasting. - Sercan Ömer Arik; Chun-Liang Li; Jinsung Yoon; Rajarishi Sinha; Arkady Epshteyn; Long T. Le; Vikas Menon; Shashank Singh 005; Leyou Zhang; Nate Yoder; Martin Nikoltchev; Yash Sonthalia; et al. (CoRR, 2020)

Dynamic-DeepHit: A Deep Learning Approach for Dynamic Survival Analysis With Competing Risks Based on Longitudinal Data. - Changhee Lee; Jinsung Yoon; Mihaela van der Schaar (IEEE Trans. Biomed. Eng., 2020)

Dynamic Prediction in Clinical Survival Analysis Using Temporal Convolutional Networks. - Daniel Jarrett; Jinsung Yoon; Mihaela van der Schaar (IEEE J. Biomed. Health Informatics, 2020)

Anonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN). - Jinsung Yoon; Lydia N. Drumright; Mihaela van der Schaar (IEEE J. Biomed. Health Informatics, 2020)

End-to-End Machine Learning Frameworks for Medicine: Data Imputation, Model Interpretation and Synthetic Data Generation. - Jinsung Yoon (2020)

CutPaste: Self-Supervised Learning for Anomaly Detection and Localization. - Chun-Liang Li; Kihyuk Sohn; Jinsung Yoon; Tomas Pfister (CVPR, 2021)

Clairvoyance: A Pipeline Toolkit for Medical Time Series. - Daniel Jarrett; Jinsung Yoon; Ioana Bica; Zhaozhi Qian; Ari Ercole; Mihaela van der Schaar (ICLR, 2021)

Learning and Evaluating Representations for Deep One-Class Classification. - Kihyuk Sohn; Chun-Liang Li; Jinsung Yoon; Minho Jin; Tomas Pfister (ICLR, 2021)

Controlling Neural Networks with Rule Representations. - Sungyong Seo; Sercan Ö. Arik; Jinsung Yoon; Xiang Zhang; Kihyuk Sohn; Tomas Pfister (NeurIPS, 2021)

6MapNet: Representing Soccer Players from Tracking Data by a Triplet Network. - Hyunsung Kim 004; Jihun Kim; Dongwook Chung; Jonghyun Lee; Jinsung Yoon; Sang-Ki Ko (MLSA@PKDD/ECML, 2021)

Self-Trained One-class Classification for Unsupervised Anomaly Detection. - Jinsung Yoon; Kihyuk Sohn; Chun-Liang Li; Sercan Ö. Arik; Chen-Yu Lee; Tomas Pfister (CoRR, 2021)

A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan. - Sercan Ö. Arik; Joel Shor; Rajarishi Sinha; Jinsung Yoon; Joseph R. Ledsam; Long T. Le; Michael W. Dusenberry; Nathanael C. Yoder; Kris Popendorf; Arkady Epshteyn; Johan Euphrosine; Elli Kanal; et al. (npj Digit. Medicine, 2021)

SoccerCPD: Formation and Role Change-Point Detection in Soccer Matches Using Spatiotemporal Tracking Data. - Hyunsung Kim 004; Bit Kim; Dongwook Chung; Jinsung Yoon; Sang-Ki Ko (KDD, 2022)

Cost-Efficient and Bias-Robust Sports Player Tracking by Integrating GPS and Video. - Hyunsung Kim 004; Changjo Kim; Minchul Jeong; Jaechan Lee; Jinsung Yoon; Sang-Ki Ko (MLSA@PKDD/ECML, 2022)

A Deep Learning Approach for Fatigue Prediction in Sports Using GPS Data and Rate of Perceived Exertion. - Jeongbin Kim 004; Hyunsung Kim 004; Jonghyun Lee; Jaechan Lee; Jinsung Yoon; Sang-Ki Ko (IEEE Access, 2022)

Towards Group Robustness in the presence of Partial Group Labels. - Vishnu Suresh Lokhande; Kihyuk Sohn; Jinsung Yoon; Madeleine Udell; Chen-Yu Lee; Tomas Pfister (CoRR, 2022)

Interpretable Mixture of Experts for Structured Data. - Aya Abdelsalam Ismail; Sercan Ö. Arik; Jinsung Yoon; Ankur Taly; Soheil Feizi; Tomas Pfister (CoRR, 2022)

Algorithmic fairness in pandemic forecasting: lessons from COVID-19. - Thomas C. Tsai; Sercan Ö. Arik; Benjamin H. Jacobson; Jinsung Yoon; Nate Yoder; Dario Sava; Margaret Mitchell; Garth Graham; Tomas Pfister (npj Digit. Medicine, 2022)

LIMIS: Locally Interpretable Modeling using Instance-wise Subsampling. - Jinsung Yoon; Sercan Ö. Arik; Tomas Pfister (Trans. Mach. Learn. Res., 2022)

Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection. - Jinsung Yoon; Kihyuk Sohn; Chun-Liang Li; Sercan Ö. Arik; Chen-Yu Lee; Tomas Pfister (Trans. Mach. Learn. Res., 2022)

Rediscovery of CNN's Versatility for Text-based Encoding of Raw Electronic Health Records. - Min Jae Lee; Kyunghoon Hur; Jiyoun Kim; Jinsung Yoon; Edward Choi (CHIL, 2023)

A Real-Time Keyword Spotting System Based on an End-To-End Binary Convolutional Neural Network in FPGA. - Jinsung Yoon; Donghyun Lee; Neungyun Kim; Su-Jung Lee; Gil-Ho Kwak; Tae-Hwan Kim (COOL CHIPS, 2023)

Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs. - Jiefeng Chen 001; Jinsung Yoon; Sayna Ebrahimi; Sercan Ö. Arik; Tomas Pfister; Somesh Jha (EMNLP (Findings), 2023)

Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM. - Hyunsung Kim 004; Han-Jun Choi; Changjo Kim; Jinsung Yoon; Sang-Ki Ko (KDD, 2023)

Data-Efficient and Interpretable Tabular Anomaly Detection. - Chun-Hao Chang; Jinsung Yoon; Sercan Ö. Arik; Madeleine Udell; Tomas Pfister (KDD, 2023)

Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types. - Kihyuk Sohn; Jinsung Yoon; Chun-Liang Li; Chen-Yu Lee; Tomas Pfister (WACV, 2023)

Rediscovery of CNN's Versatility for Text-based Encoding of Raw Electronic Health Records. - Eunbyeol Cho; Min Jae Lee; Kyunghoon Hur; Jiyoun Kim; Jinsung Yoon; Edward Choi (CoRR, 2023)

PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series. - Nicasia Beebe-Wang; Sayna Ebrahimi; Jinsung Yoon; Sercan Ö. Arik; Tomas Pfister (CoRR, 2023)

Search-Adaptor: Text Embedding Customization for Information Retrieval. - Jinsung Yoon; Sercan Ö. Arik; Yanfei Chen; Tomas Pfister (CoRR, 2023)

EHR-Safe: generating high-fidelity and privacy-preserving synthetic electronic health records. - Jinsung Yoon; Michel J. Mizrahi; Nahid Farhady Ghalaty; Thomas Jarvinen; Ashwin S. Ravi; Peter Brune; Fanyu Kong; Dave Anderson; George Lee; Arie Meir; Farhana Bandukwala; Elli Kanal; et al. (npj Digit. Medicine, 2023)

Invariant Structure Learning for Better Generalization and Causal Explainability. - Yunhao Ge; Sercan Ö. Arik; Jinsung Yoon; Ao Xu; Laurent Itti; Tomas Pfister (Trans. Mach. Learn. Res., 2023)

Interpretable Mixture of Experts. - Aya Abdelsalam Ismail; Sercan Ö. Arik; Jinsung Yoon; Ankur Taly; Soheil Feizi; Tomas Pfister (Trans. Mach. Learn. Res., 2023)

SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch. - Jinsung Yoon; Kihyuk Sohn; Chun-Liang Li; Sercan Ö. Arik; Tomas Pfister (Trans. Mach. Learn. Res., 2023)

The Design of GNSS/IMU Loosely-Coupled Integration Filter for Wearable EPTS of Football Players. - Mingu Kim; Chulwoo Park; Jinsung Yoon (Sensors, 2023)

Search-Adaptor: Embedding Customization for Information Retrieval. - Jinsung Yoon; Yanfei Chen; Sercan Ö. Arik; Tomas Pfister (ACL (1), 2024)

Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrieval. - Yanfei Chen; Jinsung Yoon; Devendra Singh Sachan; Qingze Wang; Vincent Cohen-Addad; MohammadHossein Bateni; Chen-Yu Lee; Tomas Pfister (EMNLP (Findings), 2024)

Matryoshka-Adaptor: Unsupervised and Supervised Tuning for Smaller Embedding Dimensions. - Jinsung Yoon; Rajarishi Sinha; Sercan Ömer Arik; Tomas Pfister (EMNLP, 2024)

Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning. - Sungwon Han 001; Jinsung Yoon; Sercan Ö. Arik; Tomas Pfister (ICML, 2024)

Contextual Sprint Classification in Soccer Based on Deep Learning. - Hyunsung Kim 004; Gun-Hee Joe; Jinsung Yoon; Sang-Ki Ko (CoRR, 2024)

DBHP: Trajectory Imputation in Multi-Agent Sports Using Derivative-Based Hybrid Prediction. - Hanjun Choi; Hyunsung Kim 004; Minho Lee; Changjo Kim; Jinsung Yoon; Sang-Ki Ko (CoRR, 2024)

SKQVC: One-Shot Voice Conversion by K-Means Quantization with Self-Supervised Speech Representations. - Youngjun Sim; Jinsung Yoon; Young-Joo Suh (CoRR, 2024)

ASPEST: Bridging the Gap Between Active Learning and Selective Prediction. - Jiefeng Chen 001; Jinsung Yoon; Sayna Ebrahimi; Sercan Ö. Arik; Somesh Jha; Tomas Pfister (Trans. Mach. Learn. Res., 2024)

Provable Membership Inference Privacy. - Zachary Izzo; Jinsung Yoon; Sercan Ö. Arik; James Zou 001 (Trans. Mach. Learn. Res., 2024)

A compact RTK-GNSS device for high-precision localization of outdoor mobile robots. - Sung-Jin Lee; Bit Kim; Dongwook Yang; Junseo Kim; Tom Parkinson; Johsan Billingham; Chulwoo Park; Jinsung Yoon; Dae-Young Lee (J. Field Robotics, 2024)

Debiasing Online Preference Learning via Preference Feature Preservation. - Dongyoung Kim; Jinsung Yoon; Jinwoo Shin; Jaehyung Kim (ACL (Findings), 2025)

Embedding-Converter: A Unified Framework for Cross-Model Embedding Transformation. - Jinsung Yoon; Sercan Ö. Arik (ACL (1), 2025)

Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG. - Bowen Jin; Jinsung Yoon; Jiawei Han 001; Sercan Ö. Arik (ICLR, 2025)

Learn-by-interact: A Data-Centric Framework For Self-Adaptive Agents in Realistic Environments. - Hongjin Su; Ruoxi Sun 002; Jinsung Yoon; Pengcheng Yin; Tao Yu 009; Sercan Ö. Arik (ICLR, 2025)

BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval. - Hongjin Su; Howard Yen; Mengzhou Xia; Weijia Shi; Niklas Muennighoff; Han-yu Wang; Haisu Liu; Quan Shi; Zachary S. Siegel; Michael Tang; Ruoxi Sun 002; Jinsung Yoon; et al. (ICLR, 2025)

LLM-Guided Self-Supervised Tabular Learning With Task-Specific Pre-text Tasks. - Sungwon Han 001; Seungeon Lee 001; Meeyoung Cha; Sercan Ö. Arik; Jinsung Yoon (Trans. Mach. Learn. Res., 2025)

LLM Alignment as Retriever Optimization: An Information Retrieval Perspective. - Bowen Jin; Jinsung Yoon; Zhen Qin 001; Ziqi Wang 003; Wei Xiong 015; Yu Meng 001; Jiawei Han 001; Sercan Ö. Arik (CoRR, 2025)

Retrieval Augmented Time Series Forecasting. - Sungwon Han 001; Seungeon Lee 001; Meeyoung Cha; Sercan Ö. Arik; Jinsung Yoon (CoRR, 2025)

An Empirical Study on Reinforcement Learning for Reasoning-Search Interleaved LLM Agents. - Bowen Jin; Jinsung Yoon; Priyanka Kargupta; Sercan Ö. Arik; Jiawei Han 001 (CoRR, 2025)

Hybrid Latent Reasoning via Reinforcement Learning. - Zhenrui Yue; Bowen Jin; Huimin Zeng 001; Honglei Zhuang; Zhen Qin 001; Jinsung Yoon; Lanyu Shang; Jiawei Han 001; Dong Wang 002 (CoRR, 2025)

MLE-STAR: Machine Learning Engineering Agent via Search and Targeted Refinement. - Jaehyun Nam; Jinsung Yoon; Jiefeng Chen 001; Jinwoo Shin; Sercan Ö. Arik; Tomas Pfister (CoRR, 2025)

Maestro-EVC: Controllable Emotional Voice Conversion Guided by References and Explicit Prosody. - Jinsung Yoon; Wooyeol Jeong; Jio Gim; Young-Joo Suh (CoRR, 2025)

ELASTIC: Event-Tracking Data Synchronization in Soccer Without Annotated Event Locations. - Hyunsung Kim; Hoyoung Choi; Sangwoo Seo; Tom Boomstra; Jinsung Yoon; Chanyoung Park (CoRR, 2025)