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08/25/2023
PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series
Real-world time series data that commonly reflect sequential human behav...
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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...
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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...
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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...
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11/30/2022
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch
Semi-supervised anomaly detection is a common problem, as often the data...
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11/12/2022
Provable Membership Inference Privacy
In applications involving sensitive data, such as finance and healthcare...
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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...
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06/13/2022
Invariant Structure Learning for Better Generalization and Causal Explainability
Learning the causal structure behind data is invaluable for improving ge...
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06/05/2022
Interpretable Mixture of Experts for Structured Data
With the growth of machine learning for structured data, the need for re...
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03/03/2022
Data-Efficient and Interpretable Tabular Anomaly Detection
Anomaly detection (AD) plays an important role in numerous applications....
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01/10/2022
Towards Group Robustness in the presence of Partial Group Labels
Learning invariant representations is an important requirement when trai...
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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...
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09/10/2021
6MapNet: Representing soccer players from tracking data by a triplet network
Although the values of individual soccer players have become astronomica...
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06/14/2021
Controlling Neural Networks with Rule Representations
We propose a novel training method to integrate rules into deep learning...
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06/11/2021
Self-Trained One-class Classification for Unsupervised Anomaly Detection
Anomaly detection (AD), separating anomalies from normal data, has vario...
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04/08/2021
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
We aim at constructing a high performance model for defect detection tha...
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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...
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08/03/2020
Interpretable Sequence Learning for COVID-19 Forecasting
We propose a novel approach that integrates machine learning into compar...
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07/23/2020
Hide-and-Seek Privacy Challenge
The clinical time-series setting poses a unique combination of challenge...
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09/26/2019
RL-LIM: Reinforcement Learning-based Locally Interpretable Modeling
Understanding black-box machine learning models is important towards the...
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09/25/2019
Data Valuation using Reinforcement Learning
Quantifying the value of data is a fundamental problem in machine learni...
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06/16/2019
ASAC: Active Sensing using Actor-Critic models
Deciding what and when to observe is critical when making observations i...
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11/26/2018
MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks
Accurate prediction of disease trajectories is critical for early identi...
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11/22/2018
Feature Selection for Survival Analysis with Competing Risks using Deep Learning
Deep learning models for survival analysis have gained significant atten...
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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...
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06/07/2018
GAIN: Missing Data Imputation using Generative Adversarial Nets
We propose a novel method for imputing missing data by adapting the well...
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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...
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06/05/2017
ToPs: Ensemble Learning with Trees of Predictors
We present a new approach to ensemble learning. Our approach constructs ...
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11/16/2016
A Semi-Markov Switching Linear Gaussian Model for Censored Physiological Data
Critically ill patients in regular wards are vulnerable to unanticipated...
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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...
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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...
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12/23/2015