Search Results for author: Jinsung Yoon

Found 42 papers, 21 papers with code

Provable Membership Inference Privacy

no code implementations12 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.

SoccerCPD: Formation and Role Change-Point Detection in Soccer Matches Using Spatiotemporal Tracking Data

1 code implementation22 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.

Change Point Detection

Invariant Structure Learning for Better Generalization and Causal Explainability

no code implementations13 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.

Self-Supervised Learning

Interpretable Mixture of Experts for Structured Data

no code implementations5 Jun 2022 Aya Abdelsalam Ismail, Sercan Ö. Arik, Jinsung Yoon, Ankur Taly, Soheil Feizi, Tomas Pfister

We introduce a novel framework, Interpretable Mixture of Experts (IME), that provides interpretability for structured data while preserving accuracy.

Data-Efficient and Interpretable Tabular Anomaly Detection

no code implementations3 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.

Additive models Anomaly Detection

Towards Group Robustness in the presence of Partial Group Labels

no code implementations10 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.

Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types

no code implementations21 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.

Anomaly Detection Deep Clustering +1

Invariant Learning with Partial Group Labels

no code implementations29 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.

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

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.

Decision Making

CutPaste: Self-Supervised Learning for Anomaly Detection and Localization

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.

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

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

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.

LIMIS: Locally Interpretable Modeling using Instance-wise Subsampling

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

Understanding black-box machine learning models is crucial for their widespread adoption.

reinforcement Learning

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 +2

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).

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.

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.

Variable Selection

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.

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.

BIG-bench Machine Learning

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

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.

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.

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.

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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