b'Jinsung Yoon'

research

∙ 08/25/2023

PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series

Real-world time series data that commonly reflect sequential human behav...

0 Nicasia Beebe-Wang, et al. ∙

research

∙ 06/14/2023

Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM

As artificial intelligence spreads out to numerous fields, the applicati...

0 Hyunsung Kim, et al. ∙

research

∙ 04/07/2023

ASPEST: Bridging the Gap Between Active Learning and Selective Prediction

Selective prediction aims to learn a reliable model that abstains from m...

2 Jiefeng Chen, et al. ∙

research

∙ 03/15/2023

Rediscovery of CNN's Versatility for Text-based Encoding of Raw Electronic Health Records

Making the most use of abundant information in electronic health records...

0 Eunbyeol Cho, et al. ∙

research

∙ 11/30/2022

SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch

Semi-supervised anomaly detection is a common problem, as often the data...

0 Jinsung Yoon, et al. ∙

research

∙ 11/12/2022

Provable Membership Inference Privacy

In applications involving sensitive data, such as finance and healthcare...

0 Zachary Izzo, et al. ∙

research

∙ 06/22/2022

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

In fluid team sports such as soccer and basketball, analyzing team forma...

0 Hyunsung Kim, et al. ∙

research

∙ 06/13/2022

Invariant Structure Learning for Better Generalization and Causal Explainability

Learning the causal structure behind data is invaluable for improving ge...

0 Yunhao Ge, et al. ∙

research

∙ 06/05/2022

Interpretable Mixture of Experts for Structured Data

With the growth of machine learning for structured data, the need for re...

16 Aya Abdelsalam Ismail, et al. ∙

research

∙ 03/03/2022

Data-Efficient and Interpretable Tabular Anomaly Detection

Anomaly detection (AD) plays an important role in numerous applications....

0 Chun-Hao Chang, et al. ∙

research

∙ 01/10/2022

Towards Group Robustness in the presence of Partial Group Labels

Learning invariant representations is an important requirement when trai...

0 Vishnu Suresh Lokhande, et al. ∙

research

∙ 12/21/2021

Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types

We introduce anomaly clustering, whose goal is to group data into semant...

22 Kihyuk Sohn, et al. ∙

research

∙ 09/10/2021

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

Although the values of individual soccer players have become astronomica...

4 Hyunsung Kim, et al. ∙

research

∙ 06/14/2021

Controlling Neural Networks with Rule Representations

We propose a novel training method to integrate rules into deep learning...

0 Sungyong Seo, et al. ∙

research

∙ 06/11/2021

Self-Trained One-class Classification for Unsupervised Anomaly Detection

Anomaly detection (AD), separating anomalies from normal data, has vario...

0 Jinsung Yoon, et al. ∙

research

∙ 04/08/2021

CutPaste: Self-Supervised Learning for Anomaly Detection and Localization

We aim at constructing a high performance model for defect detection tha...

9 Chun-Liang Li, et al. ∙

research

∙ 11/04/2020

Learning and Evaluating Representations for Deep One-class Classification

We present a two-stage framework for deep one-class classification. We f...

0 Kihyuk Sohn, et al. ∙

research

∙ 08/03/2020

Interpretable Sequence Learning for COVID-19 Forecasting

We propose a novel approach that integrates machine learning into compar...

36 Sercan O. Arik, et al. ∙

research

∙ 07/23/2020

Hide-and-Seek Privacy Challenge

The clinical time-series setting poses a unique combination of challenge...

11 James Jordon, et al. ∙

research

∙ 09/26/2019

RL-LIM: Reinforcement Learning-based Locally Interpretable Modeling

Understanding black-box machine learning models is important towards the...

20 Jinsung Yoon, et al. ∙

research

∙ 09/25/2019

Data Valuation using Reinforcement Learning

Quantifying the value of data is a fundamental problem in machine learni...

11 Jinsung Yoon, et al. ∙

research

∙ 06/16/2019

ASAC: Active Sensing using Actor-Critic models

Deciding what and when to observe is critical when making observations i...

0 Jinsung Yoon, et al. ∙

research

∙ 11/26/2018

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

Accurate prediction of disease trajectories is critical for early identi...

0 Daniel Jarrett, et al. ∙

research

∙ 11/22/2018

Feature Selection for Survival Analysis with Competing Risks using Deep Learning

Deep learning models for survival analysis have gained significant atten...

0 Carl Rietschel, et al. ∙

research

∙ 06/29/2018

Measuring the quality of Synthetic data for use in competitions

Machine learning has the potential to assist many communities in using t...

0 James Jordon, et al. ∙

research

∙ 06/07/2018

GAIN: Missing Data Imputation using Generative Adversarial Nets

We propose a novel method for imputing missing data by adapting the well...

0 Jinsung Yoon, et al. ∙

research

∙ 02/18/2018

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

Training complex machine learning models for prediction often requires a...

0 Jinsung Yoon, et al. ∙

research

∙ 06/05/2017

ToPs: Ensemble Learning with Trees of Predictors

We present a new approach to ensemble learning. Our approach constructs ...

0 Jinsung Yoon, et al. ∙

research

∙ 11/16/2016

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

Critically ill patients in regular wards are vulnerable to unanticipated...

0 Ahmed M. Alaa, et al. ∙

research

∙ 10/27/2016

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

Objective: In this paper, we develop a personalized real-time risk scori...

0 Ahmed M. Alaa, et al. ∙

research

∙ 05/03/2016

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

We develop a personalized real time risk scoring algorithm that provides...

0 Ahmed M. Alaa, et al. ∙

research

∙ 12/23/2015

Adaptive Ensemble Learning with Confidence Bounds

Extracting actionable intelligence from distributed, heterogeneous, corr...

0 Cem Tekin, et al. ∙

Jinsung Yoon

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