Milos Hauskrecht | DeepAI

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∙ 08/21/2023

Personalized Event Prediction for Electronic Health Records

Clinical event sequences consist of hundreds of clinical events that rep...

0 Jeong Min Lee, et al. ∙

∙ 04/06/2022

Learning to Adapt Clinical Sequences with Residual Mixture of Experts

Clinical event sequences in Electronic Health Records (EHRs) record deta...

0 Jeong Min Lee, et al. ∙

∙ 06/28/2021

Improving Prediction of Low-Prior Clinical Events with Simultaneous General Patient-State Representation Learning

Low-prior targets are common among many important clinical events, which...

0 Matthew Barren, et al. ∙

∙ 04/05/2021

Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning

Clinical event sequences consist of thousands of clinical events that re...

0 Jeong Min Lee, et al. ∙

∙ 12/19/2019

Contextual Outlier Detection in Continuous-Time Event Sequences

Continuous-time event sequences represent discrete events occurring in c...

0 Siqi Liu, et al. ∙

∙ 08/03/2017

Detection of Abnormal Input-Output Associations

We study a novel outlier detection problem that aims to identify abnorma...

0 Charmgil Hong, et al. ∙

∙ 12/21/2016

Detecting Unusual Input-Output Associations in Multivariate Conditional Data

Despite tremendous progress in outlier detection research in recent year...

0 Charmgil Hong, et al. ∙

∙ 11/06/2015

Active Perceptual Similarity Modeling with Auxiliary Information

Learning a model of perceptual similarity from a collection of objects i...

0 Eric Heim, et al. ∙

∙ 05/15/2015

MCODE: Multivariate Conditional Outlier Detection

Outlier detection aims to identify unusual data instances that deviate f...

0 Charmgil Hong, et al. ∙

∙ 01/14/2014

Binary Classifier Calibration: Non-parametric approach

Accurate calibration of probabilistic predictive models learned is criti...

0 Mahdi Pakdaman Naeini, et al. ∙

∙ 01/13/2014

Binary Classifier Calibration: Bayesian Non-Parametric Approach

A set of probabilistic predictions is well calibrated if the events that...

0 Mahdi Pakdaman Naeini, et al. ∙

∙ 11/27/2013

Sparse Linear Dynamical System with Its Application in Multivariate Clinical Time Series

Linear Dynamical System (LDS) is an elegant mathematical framework for m...

0 Zitao Liu, et al. ∙

∙ 09/26/2013

The Bregman Variational Dual-Tree Framework

Graph-based methods provide a powerful tool set for many non-parametric ...

0 Saeed Amizadeh, et al. ∙

∙ 01/30/2013

Hierarchical Solution of Markov Decision Processes using Macro-actions

We investigate the use of temporally abstract actions, or macro-actions,...

0 Milos Hauskrecht, et al. ∙

∙ 01/10/2013

A Clustering Approach to Solving Large Stochastic Matching Problems

In this work we focus on efficient heuristics for solving a class of sto...

0 Milos Hauskrecht, et al. ∙

∙ 10/19/2012

Monte-Carlo optimizations for resource allocation problems in stochastic network systems

Real-world distributed systems and networks are often unreliable and sub...

0 Milos Hauskrecht, et al. ∙

∙ 10/16/2012

Variational Dual-Tree Framework for Large-Scale Transition Matrix Approximation

In recent years, non-parametric methods utilizing random walks on graphs...

0 Saeed Amizadeh, et al. ∙

∙ 07/11/2012

Solving Factored MDPs with Continuous and Discrete Variables

Although many real-world stochastic planning problems are more naturally...

0 Carlos E. Guestrin, et al. ∙

∙ 06/13/2012

Partitioned Linear Programming Approximations for MDPs

Approximate linear programming (ALP) is an efficient approach to solving...

0 Branislav Kveton, et al. ∙

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