
Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning
Clinical event sequences consist of thousands of clinical events that re...
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Contextual Outlier Detection in ContinuousTime Event Sequences
Continuoustime event sequences represent discrete events occurring in c...
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Detection of Abnormal InputOutput Associations
We study a novel outlier detection problem that aims to identify abnorma...
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Detecting Unusual InputOutput Associations in Multivariate Conditional Data
Despite tremendous progress in outlier detection research in recent year...
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Active Perceptual Similarity Modeling with Auxiliary Information
Learning a model of perceptual similarity from a collection of objects i...
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MCODE: Multivariate Conditional Outlier Detection
Outlier detection aims to identify unusual data instances that deviate f...
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Binary Classifier Calibration: Nonparametric approach
Accurate calibration of probabilistic predictive models learned is criti...
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Binary Classifier Calibration: Bayesian NonParametric Approach
A set of probabilistic predictions is well calibrated if the events that...
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Sparse Linear Dynamical System with Its Application in Multivariate Clinical Time Series
Linear Dynamical System (LDS) is an elegant mathematical framework for m...
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The Bregman Variational DualTree Framework
Graphbased methods provide a powerful tool set for many nonparametric ...
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Hierarchical Solution of Markov Decision Processes using Macroactions
We investigate the use of temporally abstract actions, or macroactions,...
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A Clustering Approach to Solving Large Stochastic Matching Problems
In this work we focus on efficient heuristics for solving a class of sto...
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MonteCarlo optimizations for resource allocation problems in stochastic network systems
Realworld distributed systems and networks are often unreliable and sub...
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Variational DualTree Framework for LargeScale Transition Matrix Approximation
In recent years, nonparametric methods utilizing random walks on graphs...
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Solving Factored MDPs with Continuous and Discrete Variables
Although many realworld stochastic planning problems are more naturally...
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Partitioned Linear Programming Approximations for MDPs
Approximate linear programming (ALP) is an efficient approach to solving...
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Milos Hauskrecht
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Professor of Computer Science at University of Pittsburgh