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2020 – today
- 2024
[j21]Sung-Jin Lee
, Bit Kim, Dongwook Yang, Junseo Kim, Tom Parkinson, Johsan Billingham, Chulwoo Park, Jinsung Yoon, Dae-Young Lee:
A compact RTK-GNSS device for high-precision localization of outdoor mobile robots. J. Field Robotics 41(5): 1349-1365 (2024)- [j20]Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan Ö. Arik, Somesh Jha, Tomas Pfister:
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction. Trans. Mach. Learn. Res. 2024 (2024) - [j19]Zachary Izzo, Jinsung Yoon, Sercan Ö. Arik, James Zou:
Provable Membership Inference Privacy. Trans. Mach. Learn. Res. 2024 (2024) - [c35]Jinsung Yoon, Yanfei Chen, Sercan Ö. Arik, Tomas Pfister:
Search-Adaptor: Embedding Customization for Information Retrieval. ACL (1) 2024: 12230-12247 - [c34]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. EMNLP (Findings) 2024: 4705-4726 - [c33]Jinsung Yoon, Rajarishi Sinha, Sercan Ömer Arik, Tomas Pfister:
Matryoshka-Adaptor: Unsupervised and Supervised Tuning for Smaller Embedding Dimensions. EMNLP 2024: 10318-10336 - [c32]Sungwon Han, Jinsung Yoon, Sercan Ö. Arik, Tomas Pfister:
Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning. ICML 2024 - [i45]Sungwon Han, Jinsung Yoon, Sercan Ö. Arik, Tomas Pfister:
Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning. CoRR abs/2404.09491 (2024) - [i44]Hyunsung Kim, Gun-Hee Joe, Jinsung Yoon, Sang-Ki Ko:
Contextual Sprint Classification in Soccer Based on Deep Learning. CoRR abs/2406.15659 (2024) - [i43]Hongjin Su, Howard Yen, Mengzhou Xia, Weijia Shi, Niklas Muennighoff, Han-yu Wang, Haisu Liu, Quan Shi, Zachary S. Siegel, Michael Tang, Ruoxi Sun, Jinsung Yoon, Sercan Ö. Arik, Danqi Chen, Tao Yu:
BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval. CoRR abs/2407.12883 (2024) - [i42]Jinsung Yoon, Rajarishi Sinha, Sercan Ö. Arik, Tomas Pfister:
Matryoshka-Adaptor: Unsupervised and Supervised Tuning for Smaller Embedding Dimensions. CoRR abs/2407.20243 (2024) - [i41]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. CoRR abs/2408.01875 (2024) - [i40]Hanjun Choi, Hyunsung Kim, Minho Lee, Changjo Kim, Jinsung Yoon, Sang-Ki Ko:
DBHP: Trajectory Imputation in Multi-Agent Sports Using Derivative-Based Hybrid Prediction. CoRR abs/2408.10878 (2024) - [i39]Bowen Jin, Jinsung Yoon, Jiawei Han, Sercan Ö. Arik:
Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG. CoRR abs/2410.05983 (2024) - 2023
- [j18]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, Sercan Ö. Arik, Tomas Pfister:
EHR-Safe: generating high-fidelity and privacy-preserving synthetic electronic health records. npj Digit. Medicine 6 (2023) - [j17]Mingu Kim, Chulwoo Park, Jinsung Yoon:
The Design of GNSS/IMU Loosely-Coupled Integration Filter for Wearable EPTS of Football Players. Sensors 23(4): 1749 (2023) - [j16]Yunhao Ge, Sercan Ö. Arik, Jinsung Yoon, Ao Xu, Laurent Itti, Tomas Pfister:
Invariant Structure Learning for Better Generalization and Causal Explainability. Trans. Mach. Learn. Res. 2023 (2023) - [j15]Aya Abdelsalam Ismail, Sercan Ö. Arik, Jinsung Yoon, Ankur Taly, Soheil Feizi, Tomas Pfister:
Interpretable Mixture of Experts. Trans. Mach. Learn. Res. 2023 (2023) - [j14]Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan Ö. Arik, Tomas Pfister:
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch. Trans. Mach. Learn. Res. 2023 (2023) - [c31]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. CHIL 2023: 294-313 - [c30]Jinsung Yoon, Donghyun Lee, Neungyun Kim, Su-Jung Lee, Gil-Ho Kwak, Tae-Hwan Kim:
A Real-Time Keyword Spotting System Based on an End-To-End Binary Convolutional Neural Network in FPGA. COOL CHIPS 2023: 1-3 - [c29]Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan Ö. Arik, Tomas Pfister, Somesh Jha:
Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs. EMNLP (Findings) 2023: 5190-5213 - [c28]Chun-Hao Chang, Jinsung Yoon, Sercan Ö. Arik, Madeleine Udell, Tomas Pfister:
Data-Efficient and Interpretable Tabular Anomaly Detection. KDD 2023: 190-201 - [c27]Hyunsung Kim, Han-Jun Choi, Changjo Kim, Jinsung Yoon, Sang-Ki Ko:
Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM. KDD 2023: 4296-4307 - [c26]Kihyuk Sohn, Jinsung Yoon, Chun-Liang Li, Chen-Yu Lee, Tomas Pfister:
Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types. WACV 2023: 5468-5479 - [i38]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. CoRR abs/2303.08290 (2023) - [i37]Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan Ö. Arik, Somesh Jha, Tomas Pfister:
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction. CoRR abs/2304.03870 (2023) - [i36]Hyunsung Kim, Han-Jun Choi, Changjo Kim, Jinsung Yoon, Sang-Ki Ko:
Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM. CoRR abs/2306.08206 (2023) - [i35]Nicasia Beebe-Wang, Sayna Ebrahimi, Jinsung Yoon, Sercan Ö. Arik, Tomas Pfister:
PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series. CoRR abs/2308.13703 (2023) - [i34]Jinsung Yoon, Sercan Ö. Arik, Yanfei Chen, Tomas Pfister:
Search-Adaptor: Text Embedding Customization for Information Retrieval. CoRR abs/2310.08750 (2023) - [i33]Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan Ö. Arik, Tomas Pfister, Somesh Jha:
Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs. CoRR abs/2310.11689 (2023) - [i32]Daniel Jarrett, Jinsung Yoon, Ioana Bica, Zhaozhi Qian, Ari Ercole, Mihaela van der Schaar:
Clairvoyance: A Pipeline Toolkit for Medical Time Series. CoRR abs/2310.18688 (2023) - 2022
- [j13]Jeongbin Kim, Hyunsung Kim, Jonghyun Lee, Jaechan Lee, Jinsung Yoon, Sang-Ki Ko:
A Deep Learning Approach for Fatigue Prediction in Sports Using GPS Data and Rate of Perceived Exertion. IEEE Access 10: 103056-103064 (2022) - [j12]Thomas C. Tsai, Sercan Ö. Arik, Benjamin H. Jacobson, Jinsung Yoon, Nate Yoder, Dario Sava, Margaret Mitchell, Garth Graham, Tomas Pfister:
Algorithmic fairness in pandemic forecasting: lessons from COVID-19. npj Digit. Medicine 5 (2022) - [j11]Jinsung Yoon, Sercan Ö. Arik, Tomas Pfister:
LIMIS: Locally Interpretable Modeling using Instance-wise Subsampling. Trans. Mach. Learn. Res. 2022 (2022) - [j10]Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan Ö. Arik, Chen-Yu Lee, Tomas Pfister:
Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection. Trans. Mach. Learn. Res. 2022 (2022) - [c25]Hyunsung Kim, Bit Kim, Dongwook Chung, Jinsung Yoon, Sang-Ki Ko:
SoccerCPD: Formation and Role Change-Point Detection in Soccer Matches Using Spatiotemporal Tracking Data. KDD 2022: 3146-3156 - [c24]Hyunsung Kim, Changjo Kim, Minchul Jeong, Jaechan Lee, Jinsung Yoon, Sang-Ki Ko:
Cost-Efficient and Bias-Robust Sports Player Tracking by Integrating GPS and Video. MLSA@PKDD/ECML 2022: 74-86 - [i31]Vishnu Suresh Lokhande, Kihyuk Sohn, Jinsung Yoon, Madeleine Udell, Chen-Yu Lee, Tomas Pfister:
Towards Group Robustness in the presence of Partial Group Labels. CoRR abs/2201.03668 (2022) - [i30]Chun-Hao Chang, Jinsung Yoon, Sercan Ö. Arik, Madeleine Udell, Tomas Pfister:
Data-Efficient and Interpretable Tabular Anomaly Detection. CoRR abs/2203.02034 (2022) - [i29]Aya Abdelsalam Ismail, Sercan Ö. Arik, Jinsung Yoon, Ankur Taly, Soheil Feizi, Tomas Pfister:
Interpretable Mixture of Experts for Structured Data. CoRR abs/2206.02107 (2022) - [i28]Yunhao Ge, Sercan Ö. Arik, Jinsung Yoon, Ao Xu, Laurent Itti, Tomas Pfister:
Invariant Structure Learning for Better Generalization and Causal Explainability. CoRR abs/2206.06469 (2022) - [i27]Zachary Izzo, Jinsung Yoon, Sercan Ö. Arik, James Zou:
Provable Membership Inference Privacy. CoRR abs/2211.06582 (2022) - [i26]Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan Ö. Arik, Tomas Pfister:
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch. CoRR abs/2212.00173 (2022) - 2021
- [j9]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, Isaac Jones, Chun-Liang Li, Beth Luan, Joe Mckenna, Vikas Menon, Shashank Singh, Mimi Sun, Ashwin Sura Ravi, Leyou Zhang, Dario Sava, Kane Cunningham, Hiroki Kayama, Thomas C. Tsai, Daisuke Yoneoka, Shuhei Nomura, Hiroaki Miyata, Tomas Pfister:
A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan. npj Digit. Medicine 4 (2021) - [c23]Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, Tomas Pfister:
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization. CVPR 2021: 9664-9674 - [c22]Daniel Jarrett, Jinsung Yoon, Ioana Bica, Zhaozhi Qian, Ari Ercole, Mihaela van der Schaar:
Clairvoyance: A Pipeline Toolkit for Medical Time Series. ICLR 2021 - [c21]Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon, Minho Jin, Tomas Pfister:
Learning and Evaluating Representations for Deep One-Class Classification. ICLR 2021 - [c20]Sungyong Seo, Sercan Ö. Arik, Jinsung Yoon, Xiang Zhang, Kihyuk Sohn, Tomas Pfister:
Controlling Neural Networks with Rule Representations. NeurIPS 2021: 11196-11207 - [c19]Hyunsung Kim, Jihun Kim, Dongwook Chung, Jonghyun Lee, Jinsung Yoon, Sang-Ki Ko:
6MapNet: Representing Soccer Players from Tracking Data by a Triplet Network. MLSA@PKDD/ECML 2021: 3-14 - [i25]Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, Tomas Pfister:
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization. CoRR abs/2104.04015 (2021) - [i24]Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan Ö. Arik, Chen-Yu Lee, Tomas Pfister:
Self-Trained One-class Classification for Unsupervised Anomaly Detection. CoRR abs/2106.06115 (2021) - [i23]Sungyong Seo, Sercan Ö. Arik, Jinsung Yoon, Xiang Zhang, Kihyuk Sohn, Tomas Pfister:
Controlling Neural Networks with Rule Representations. CoRR abs/2106.07804 (2021) - [i22]Hyunsung Kim, Jihun Kim, Dongwook Chung, Jonghyun Lee, Jinsung Yoon, Sang-Ki Ko:
6MapNet: Representing soccer players from tracking data by a triplet network. CoRR abs/2109.04720 (2021) - [i21]Kihyuk Sohn, Jinsung Yoon, Chun-Liang Li, Chen-Yu Lee, Tomas Pfister:
Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types. CoRR abs/2112.11573 (2021) - 2020
- [b1]Jinsung Yoon:
End-to-End Machine Learning Frameworks for Medicine: Data Imputation, Model Interpretation and Synthetic Data Generation. University of California, Los Angeles, USA, 2020 - [j8]Changhee Lee, Jinsung Yoon, Mihaela van der Schaar:
Dynamic-DeepHit: A Deep Learning Approach for Dynamic Survival Analysis With Competing Risks Based on Longitudinal Data. IEEE Trans. Biomed. Eng. 67(1): 122-133 (2020) - [j7]Daniel Jarrett, Jinsung Yoon, Mihaela van der Schaar:
Dynamic Prediction in Clinical Survival Analysis Using Temporal Convolutional Networks. IEEE J. Biomed. Health Informatics 24(2): 424-436 (2020) - [j6]Jinsung Yoon, Lydia N. Drumright, Mihaela van der Schaar:
Anonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN). IEEE J. Biomed. Health Informatics 24(8): 2378-2388 (2020) - [c18]Jinsung Yoon, Sercan Ömer Arik, Tomas Pfister:
Data Valuation using Reinforcement Learning. ICML 2020: 10842-10851 - [c17]Sercan Ömer Arik, Chun-Liang Li, Jinsung Yoon, Rajarishi Sinha, Arkady Epshteyn, Long T. Le, Vikas Menon, Shashank Singh, Leyou Zhang, Martin Nikoltchev, Yash Sonthalia, Hootan Nakhost, Elli Kanal, Tomas Pfister:
Interpretable Sequence Learning for Covid-19 Forecasting. NeurIPS 2020 - [c16]James Jordon, Daniel Jarrett, Evgeny Saveliev, Jinsung Yoon, Paul W. G. Elbers, Patrick Thoral, Ari Ercole, Cheng Zhang, Danielle Belgrave, Mihaela van der Schaar:
Hide-and-Seek Privacy Challenge: Synthetic Data Generation vs. Patient Re-identification. NeurIPS (Competition and Demos) 2020: 206-215 - [c15]Jinsung Yoon, Yao Zhang, James Jordon, Mihaela van der Schaar:
VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain. NeurIPS 2020 - [i20]James Jordon, Daniel Jarrett, Jinsung Yoon, Tavian Barnes, Paul W. G. Elbers, Patrick Thoral, Ari Ercole, Cheng Zhang, Danielle Belgrave, Mihaela van der Schaar:
Hide-and-Seek Privacy Challenge. CoRR abs/2007.12087 (2020) - [i19]Sercan Ömer 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, Tomas Pfister:
Interpretable Sequence Learning for COVID-19 Forecasting. CoRR abs/2008.00646 (2020) - [i18]Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon, Minho Jin, Tomas Pfister:
Learning and Evaluating Representations for Deep One-class Classification. CoRR abs/2011.02578 (2020)
2010 – 2019
- 2019
- [j5]Jinsung Yoon, William R. Zame, Mihaela van der Schaar:
Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networks. IEEE Trans. Biomed. Eng. 66(5): 1477-1490 (2019) - [c14]James Jordon, Jinsung Yoon, Mihaela van der Schaar:
KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks. ICLR 2019 - [c13]James Jordon, Jinsung Yoon, Mihaela van der Schaar:
PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees. ICLR (Poster) 2019 - [c12]Jinsung Yoon, James Jordon, Mihaela van der Schaar:
INVASE: Instance-wise Variable Selection using Neural Networks. ICLR (Poster) 2019 - [c11]Jinsung Yoon, James Jordon, Mihaela van der Schaar:
ASAC: Active Sensing using Actor-Critic models. MLHC 2019: 451-473 - [c10]James Jordon, Jinsung Yoon, Mihaela van der Schaar:
Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate. NeurIPS 2019: 4325-4334 - [c9]Jinsung Yoon, Daniel Jarrett, Mihaela van der Schaar:
Time-series Generative Adversarial Networks. NeurIPS 2019: 5509-5519 - [i17]Jinsung Yoon, James Jordon, Mihaela van der Schaar:
ASAC: Active Sensing using Actor-Critic models. CoRR abs/1906.06796 (2019) - [i16]Jinsung Yoon, Sercan Ömer Arik, Tomas Pfister:
Data Valuation using Reinforcement Learning. CoRR abs/1909.11671 (2019) - [i15]Jinsung Yoon, Sercan Ömer Arik, Tomas Pfister:
RL-LIM: Reinforcement Learning-based Locally Interpretable Modeling. CoRR abs/1909.12367 (2019) - 2018
- [j4]Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar:
Personalized Risk Scoring for Critical Care Prognosis Using Mixtures of Gaussian Processes. IEEE Trans. Biomed. Eng. 65(1): 207-218 (2018) - [j3]Jinsung Yoon, William R. Zame, Mihaela van der Schaar:
ToPs: Ensemble Learning With Trees of Predictors. IEEE Trans. Signal Process. 66(8): 2141-2152 (2018) - [c8]Changhee Lee, William R. Zame, Jinsung Yoon, Mihaela van der Schaar:
DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks. AAAI 2018: 2314-2321 - [c7]Jinsung Yoon, James Jordon, Mihaela van der Schaar:
GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets. ICLR (Poster) 2018 - [c6]Jinsung Yoon, William R. Zame, Mihaela van der Schaar:
Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks. ICLR (Poster) 2018 - [c5]Jinsung Yoon, James Jordon, Mihaela van der Schaar:
GAIN: Missing Data Imputation using Generative Adversarial Nets. ICML 2018: 5675-5684 - [c4]Jinsung Yoon, James Jordon, Mihaela van der Schaar:
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks. ICML 2018: 5685-5693 - [p1]Honglei Li, Yanzhou Liu, Kishan Sudusinghe, Jinsung Yoon, Erik Blasch, Mihaela van der Schaar, Shuvra S. Bhattacharyya:
Design of a Dynamic Data-Driven System for Multispectral Video Processing. Handbook of Dynamic Data Driven Applications Systems 2018: 529-545 - [i14]Jinsung Yoon, James Jordon, Mihaela van der Schaar:
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks. CoRR abs/1802.06403 (2018) - [i13]Jinsung Yoon, James Jordon, Mihaela van der Schaar:
GAIN: Missing Data Imputation using Generative Adversarial Nets. CoRR abs/1806.02920 (2018) - [i12]James Jordon, Jinsung Yoon, Mihaela van der Schaar:
Measuring the quality of Synthetic data for use in competitions. CoRR abs/1806.11345 (2018) - [i11]Carl Rietschel, Jinsung Yoon, Mihaela van der Schaar:
Feature Selection for Survival Analysis with Competing Risks using Deep Learning. CoRR abs/1811.09317 (2018) - [i10]Daniel Jarrett, Jinsung Yoon, Mihaela van der Schaar:
MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks. CoRR abs/1811.10746 (2018) - 2017
- [j2]Jinsung Yoon, Camelia Davtyan, Mihaela van der Schaar:
Discovery and Clinical Decision Support for Personalized Healthcare. IEEE J. Biomed. Health Informatics 21(4): 1133-1145 (2017) - [j1]Cem Tekin, Jinsung Yoon, Mihaela van der Schaar:
Adaptive Ensemble Learning With Confidence Bounds. IEEE Trans. Signal Process. 65(4): 888-903 (2017) - [c3]Jinsung Yoon, Ahmed M. Alaa, Martin Cadeiras, Mihaela van der Schaar:
Personalized Donor-Recipient Matching for Organ Transplantation. AAAI 2017: 1647-1654 - [i9]Jinsung Yoon, William R. Zame, Amitava Banerjee, Martin Cadeiras, Ahmed M. Alaa, Mihaela van der Schaar:
Personalized Survival Predictions for Cardiac Transplantation via Trees of Predictors. CoRR abs/1704.03458 (2017) - [i8]Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar:
Individualized Risk Prognosis for Critical Care Patients: A Multi-task Gaussian Process Model. CoRR abs/1705.07674 (2017) - [i7]Jinsung Yoon, William R. Zame, Mihaela van der Schaar:
ToPs: Ensemble Learning with Trees of Predictors. CoRR abs/1706.01396 (2017) - [i6]Jinsung Yoon, William R. Zame, Mihaela van der Schaar:
Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks. CoRR abs/1711.08742 (2017) - 2016
- [c2]Cem Tekin, Jinsung Yoon, Mihaela van der Schaar:
Adaptive Ensemble Learning with Confidence Bounds for Personalized Diagnosis. AAAI Workshop: Expanding the Boundaries of Health Informatics Using AI 2016 - [c1]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. ICML 2016: 1680-1689 - [i5]Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar:
Personalized Risk Scoring for Critical Care Patients using Mixtures of Gaussian Process Experts. CoRR abs/1605.00959 (2016) - [i4]Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar:
Personalized Risk Scoring for Critical Care Prognosis using Mixtures of Gaussian Processes. CoRR abs/1610.08853 (2016) - [i3]Jinsung Yoon, Ahmed M. Alaa, Martin Cadeiras, Mihaela van der Schaar:
Personalized Donor-Recipient Matching for Organ Transplantation. CoRR abs/1611.03934 (2016) - [i2]Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar:
A Semi-Markov Switching Linear Gaussian Model for Censored Physiological Data. CoRR abs/1611.05146 (2016) - 2015
- [i1]Cem Tekin, Jinsung Yoon, Mihaela van der Schaar:
Adaptive Ensemble Learning with Confidence Bounds. CoRR abs/1512.07446 (2015)
Coauthor Index
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