1 code implementation • 15 Apr 2024 • Sungwon Han, Jinsung Yoon, Sercan O Arik, Tomas Pfister
The proposed FeatLLM framework only uses this simple predictive model with the discovered features at inference time.
1 code implementation • ICLR 2021 • Daniel Jarrett, Jinsung Yoon, Ioana Bica, Zhaozhi Qian, Ari Ercole, Mihaela van der Schaar
Despite exponential growth in electronic patient data, there is a remarkable gap between the potential and realized utilization of ML for clinical research and decision support.
no code implementations • 18 Oct 2023 • Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan O Arik, Tomas Pfister, Somesh Jha
Large language models (LLMs) have recently shown great advances in a variety of tasks, including natural language understanding and generation.
no code implementations • 12 Oct 2023 • Jinsung Yoon, Sercan O Arik, Yanfei Chen, Tomas Pfister
Embeddings extracted by pre-trained Large Language Models (LLMs) have significant potential to improve information retrieval and search.
1 code implementation • 25 Aug 2023 • Nicasia Beebe-Wang, Sayna Ebrahimi, Jinsung Yoon, Sercan O. Arik, Tomas Pfister
In this paper, we present PAITS (Pretraining and Augmentation for Irregularly-sampled Time Series), a framework for identifying suitable pretraining strategies for sparse and irregularly sampled time series datasets.
1 code implementation • 14 Jun 2023 • Hyunsung Kim, Han-Jun Choi, Chang Jo Kim, Jinsung Yoon, Sang-Ki Ko
As artificial intelligence spreads out to numerous fields, the application of AI to sports analytics is also in the spotlight.
1 code implementation • 7 Apr 2023 • Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan Arik, Somesh Jha, Tomas Pfister
In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain while increasing accuracy and coverage.
1 code implementation • 15 Mar 2023 • Eunbyeol Cho, Min Jae Lee, Kyunghoon Hur, Jiyoun Kim, Jinsung Yoon, Edward Choi
Making the most use of abundant information in electronic health records (EHR) is rapidly becoming an important topic in the medical domain.
no code implementations • 30 Nov 2022 • Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan O. Arik, Tomas Pfister
Semi-supervised anomaly detection is a common problem, as often the datasets containing anomalies are partially labeled.
Semi-supervised Anomaly Detection Supervised Anomaly Detection
no code implementations • 12 Nov 2022 • Zachary Izzo, Jinsung Yoon, Sercan O. Arik, James Zou
However, DP's strong theoretical guarantees often come at the cost of a large drop in its utility for machine learning, and DP guarantees themselves can be difficult to interpret.
1 code implementation • 22 Jun 2022 • Hyunsung Kim, Bit Kim, Dongwook Chung, Jinsung Yoon, Sang-Ki Ko
In fluid team sports such as soccer and basketball, analyzing team formation is one of the most intuitive ways to understand tactics from domain participants' point of view.
no code implementations • 13 Jun 2022 • Yunhao Ge, Sercan Ö. Arik, Jinsung Yoon, Ao Xu, Laurent Itti, Tomas Pfister
ISL splits the data into different environments, and learns a structure that is invariant to the target across different environments by imposing a consistency constraint.
no code implementations • 5 Jun 2022 • Aya Abdelsalam Ismail, Sercan Ö. Arik, Jinsung Yoon, Ankur Taly, Soheil Feizi, Tomas Pfister
In addition to constituting a standalone inherently-interpretable architecture, IME has the premise of being integrated with existing DNNs to offer interpretability to a subset of samples while maintaining the accuracy of the DNNs.
no code implementations • 3 Mar 2022 • Chun-Hao Chang, Jinsung Yoon, Sercan Arik, Madeleine Udell, Tomas Pfister
In addition, the proposed framework, DIAD, can incorporate a small amount of labeled data to further boost anomaly detection performances in semi-supervised settings.
no code implementations • 10 Jan 2022 • Vishnu Suresh Lokhande, Kihyuk Sohn, Jinsung Yoon, Madeleine Udell, Chen-Yu Lee, Tomas Pfister
Such a requirement is impractical in situations where the data labeling efforts for minority or rare groups are significantly laborious or where the individuals comprising the dataset choose to conceal sensitive information.
2 code implementations • 21 Dec 2021 • Kihyuk Sohn, Jinsung Yoon, Chun-Liang Li, Chen-Yu Lee, Tomas Pfister
We define a distance function between images, each of which is represented as a bag of embeddings, by the Euclidean distance between weighted averaged embeddings.
no code implementations • 29 Sep 2021 • Vishnu Suresh Lokhande, Kihyuk Sohn, Jinsung Yoon, Madeleine Udell, Chen-Yu Lee, Tomas Pfister
Such a requirement is impractical in situations where the data labelling efforts for minority or rare groups is significantly laborious or where the individuals comprising the dataset choose to conceal sensitive information.
no code implementations • 10 Sep 2021 • Hyunsung Kim, Jihun Kim, Dongwook Chung, Jonghyun Lee, Jinsung Yoon, Sang-Ki Ko
Although the values of individual soccer players have become astronomical, subjective judgments still play a big part in the player analysis.
1 code implementation • NeurIPS 2021 • Sungyong Seo, Sercan O. Arik, Jinsung Yoon, Xiang Zhang, Kihyuk Sohn, Tomas Pfister
The key aspect of DeepCTRL is that it does not require retraining to adapt the rule strength -- at inference, the user can adjust it based on the desired operation point on accuracy vs. rule verification ratio.
no code implementations • 11 Jun 2021 • Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan O. Arik, Chen-Yu Lee, Tomas Pfister
We demonstrate our method on various unsupervised AD tasks with image and tabular data.
2 code implementations • CVPR 2021 • Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, Tomas Pfister
We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data.
Ranked #55 on Anomaly Detection on MVTec AD
1 code implementation • NeurIPS 2020 • Jinsung Yoon, Yao Zhang, James Jordon, Mihaela van der Schaar
We also introduce a novel tabular data augmentation method for self- and semi-supervised learning frameworks.
1 code implementation • ICLR 2021 • Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon, Minho Jin, Tomas Pfister
We first learn self-supervised representations from one-class data, and then build one-class classifiers on learned representations.
Ranked #7 on Anomaly Detection on One-class CIFAR-100
no code implementations • NeurIPS 2020 • 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, Tomas Pfister
We propose a novel approach that integrates machine learning into compartmental disease modeling to predict the progression of COVID-19.
1 code implementation • 23 Jul 2020 • James Jordon, Daniel Jarrett, Jinsung Yoon, Tavian Barnes, Paul Elbers, Patrick Thoral, Ari Ercole, Cheng Zhang, Danielle Belgrave, Mihaela van der Schaar
The clinical time-series setting poses a unique combination of challenges to data modeling and sharing.
1 code implementation • 1 Dec 2019 • Jinsung Yoon, Daniel Jarrett, Mihaela van der Schaar
A good generative model for time-series data should preservetemporal dynamics, in the sense that new sequences respect the original relationships between variablesacross time.
1 code implementation • NeurIPS 2019 • James Jordon, Jinsung Yoon, Mihaela van der Schaar
The second benefit is that, through analysis that we provide inthe paper, we can derive tighter differential privacy guarantees when several queriesare made to this mechanism.
no code implementations • NeurIPS 2019 • Jinsung Yoon, Daniel Jarrett, M Van Der Schaar
A good generative model for time-series data should preserve temporal dynamics, in the sense that new sequences respect the original relationships between variables across time.
1 code implementation • 26 Sep 2019 • Jinsung Yoon, Sercan O. Arik, Tomas Pfister
Understanding black-box machine learning models is crucial for their widespread adoption.
1 code implementation • ICML 2020 • Jinsung Yoon, Sercan O. Arik, Tomas Pfister
To adaptively learn data values jointly with the target task predictor model, we propose a meta learning framework which we name Data Valuation using Reinforcement Learning (DVRL).
1 code implementation • 16 Jun 2019 • Jinsung Yoon, James Jordon, Mihaela van der Schaar
The predictor network uses the observations selected by the selector network to predict a label, providing feedback to the selector network (well-selected variables should be predictive of the label).
1 code implementation • ICLR 2019 • James Jordon, Jinsung Yoon, Mihaela van der Schaar
We demonstrate the capability of our model to perform feature selection, showing that it performs as well as the originally proposed knockoff generation model in the Gaussian setting and that it outperforms the original model in non-Gaussian settings, including on a real-world dataset.
1 code implementation • ICLR 2019 • Jinsung Yoon, James Jordon, Mihaela van der Schaar
The advent of big data brings with it data with more and more dimensions and thus a growing need to be able to efficiently select which features to use for a variety of problems.
1 code implementation • ICLR 2019 • Jinsung Yoon, James Jordon, Mihaela van der Schaar
Machine learning has the potential to assist many communities in using the large datasets that are becoming more and more available.
Ranked #2 on Synthetic Data Generation on UCI Epileptic Seizure Recognition (using extra training data)
no code implementations • 26 Nov 2018 • Daniel Jarrett, Jinsung Yoon, Mihaela van der Schaar
Accurate prediction of disease trajectories is critical for early identification and timely treatment of patients at risk.
1 code implementation • 22 Nov 2018 • Carl Rietschel, Jinsung Yoon, Mihaela van der Schaar
Deep learning models for survival analysis have gained significant attention in the literature, but they suffer from severe performance deficits when the dataset contains many irrelevant features.
1 code implementation • 29 Jun 2018 • James Jordon, Jinsung Yoon, Mihaela van der Schaar
Machine learning has the potential to assist many communities in using the large datasets that are becoming more and more available.
8 code implementations • ICML 2018 • Jinsung Yoon, James Jordon, Mihaela van der Schaar
Accordingly, we call our method Generative Adversarial Imputation Nets (GAIN).
2 code implementations • ICML 2018 • Jinsung Yoon, James Jordon, Mihaela van der Schaar
Training complex machine learning models for prediction often requires a large amount of data that is not always readily available.
2 code implementations • ICLR 2018 • Jinsung Yoon, James Jordon, Mihaela van der Schaar
Estimating individualized treatment effects (ITE) is a challenging task due to the need for an individual's potential outcomes to be learned from biased data and without having access to the counterfactuals.
Ranked #2 on Causal Inference on Jobs
1 code implementation • ICLR 2018 • Jinsung Yoon, William R. Zame, Mihaela van der Schaar
At runtime, the operator prescribes a performance level or a cost constraint, and Deep Sensing determines what measurements to take and what to infer from those measurements, and then issues predictions.
2 code implementations • 23 Nov 2017 • Jinsung Yoon, William R. Zame, Mihaela van der Schaar
Existing methods address this estimation problem by interpolating within data streams or imputing across data streams (both of which ignore important information) or ignoring the temporal aspect of the data and imposing strong assumptions about the nature of the data-generating process and/or the pattern of missing data (both of which are especially problematic for medical data).
no code implementations • 5 Jun 2017 • Jinsung Yoon, William R. Zame, Mihaela van der Schaar
Our approach constructs a tree of subsets of the feature space and associates a predictor (predictive model) - determined by training one of a given family of base learners on an endogenously determined training set - to each node of the tree; we call the resulting object a tree of predictors.
no code implementations • 22 May 2017 • Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar
We report the development and validation of a data-driven real-time risk score that provides timely assessments for the clinical acuity of ward patients based on their temporal lab tests and vital signs, which allows for timely intensive care unit (ICU) admissions.
no code implementations • 16 Nov 2016 • Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar
Critically ill patients in regular wards are vulnerable to unanticipated clinical dete- rioration which requires timely transfer to the intensive care unit (ICU).
no code implementations • 12 Nov 2016 • Jinsung Yoon, Ahmed M. Alaa, Martin Cadeiras, Mihaela van der Schaar
Organ transplants can improve the life expectancy and quality of life for the recipient but carries the risk of serious post-operative complications, such as septic shock and organ rejection.
no code implementations • 27 Oct 2016 • Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar
Objective: In this paper, we develop a personalized real-time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs; the proposed risk scoring system ensures timely intensive care unit (ICU) admissions for clinically deteriorating patients.
no code implementations • 3 May 2016 • Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar
We develop a personalized real time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs.
no code implementations • 23 Dec 2015 • Cem Tekin, Jinsung Yoon, Mihaela van der Schaar
Extracting actionable intelligence from distributed, heterogeneous, correlated and high-dimensional data sources requires run-time processing and learning both locally and globally.