Search Results for author: Jinsung Yoon

Found 33 papers, 19 papers with code

6MapNet: Representing soccer players from tracking data by a triplet network

no code implementations10 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.

Controlling Neural Networks with Rule Representations

no code implementations14 Jun 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.

Decision Making

Self-Trained One-class Classification for Unsupervised Anomaly Detection

no code implementations11 Jun 2021 Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan O. Arik, Chen-Yu Lee, Tomas Pfister

In experiments, we show the efficacy of our method for unsupervised anomaly detection on benchmarks from image and tabular data domains.

Classification Unsupervised Anomaly Detection

CutPaste: Self-Supervised Learning for Anomaly Detection and Localization

1 code implementation 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 #7 on Anomaly Detection on MVTec AD (using extra training data)

Anomaly Detection Data Augmentation +4

Clairvoyance: A Pipeline Toolkit for Medical Time Series

no code implementations 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.

AutoML Time Series

Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate

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.

Time-series Generative Adversarial Networks

1 code implementation1 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.

Time Series

Time-series Generative Adversarial Networks

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.

Time Series

RL-LIM: Reinforcement Learning-based Locally Interpretable Modeling

1 code implementation26 Sep 2019 Jinsung Yoon, Sercan O. Arik, Tomas Pfister

RL-LIM employs reinforcement learning to select a small number of samples and distill the black-box model prediction into a low-capacity locally interpretable model.

Data Valuation using Reinforcement Learning

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).

Domain Adaptation Meta-Learning

ASAC: Active Sensing using Actor-Critic models

1 code implementation16 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).

INVASE: Instance-wise Variable Selection using Neural Networks

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.

Feature Selection Variable Selection

KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks

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.

Feature Selection

PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees

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.

Synthetic Data Generation

MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks

no code implementations26 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.

Survival Analysis

Feature Selection for Survival Analysis with Competing Risks using Deep Learning

1 code implementation22 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.

Feature Selection Survival Analysis

Measuring the quality of Synthetic data for use in competitions

1 code implementation29 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.

RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks

1 code implementation 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.

Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks

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.

GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets

1 code implementation 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.

Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks

1 code implementation23 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).

Matrix Completion Multivariate Time Series Imputation

ToPs: Ensemble Learning with Trees of Predictors

no code implementations5 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.

Ensemble Learning

Individualized Risk Prognosis for Critical Care Patients: A Multi-task Gaussian Process Model

no code implementations22 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.

A Semi-Markov Switching Linear Gaussian Model for Censored Physiological Data

no code implementations16 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).

Personalized Donor-Recipient Matching for Organ Transplantation

no code implementations12 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.

Personalized Risk Scoring for Critical Care Prognosis using Mixtures of Gaussian Processes

no code implementations27 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.

Gaussian Processes Transfer Learning

Personalized Risk Scoring for Critical Care Patients using Mixtures of Gaussian Process Experts

no code implementations3 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.

Transfer Learning

Adaptive Ensemble Learning with Confidence Bounds

no code implementations23 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.

Ensemble Learning Meta-Learning

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