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03/07/2023
Variational Inference for Neyman-Scott Processes
Neyman-Scott processes (NSPs) have been applied across a range of fields...
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11/06/2021
Deep Neyman-Scott Processes
A Neyman-Scott process is a special case of a Cox process. The latent an...
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10/27/2021
Convolutional Deep Exponential Families
We describe convolutional deep exponential families (CDEFs) in this pape...
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01/16/2014
Intrusion Detection using Continuous Time Bayesian Networks
Intrusion detection systems (IDSs) fall into two high-level categories: ...
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01/10/2013
Policy Improvement for POMDPs Using Normalized Importance Sampling
We present a new method for estimating the expected return of a POMDP fr...
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12/12/2012
Reinforcement Learning with Partially Known World Dynamics
Reinforcement learning would enjoy better success on real-world problems...
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12/12/2012
Continuous Time Bayesian Networks
In this paper we present a language for finite state continuous time Bay...
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10/19/2012
Learning Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBNs) describe structured stochastic...
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07/04/2012
Expectation Maximization and Complex Duration Distributions for Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBNs) describe structured stochastic...
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07/04/2012
Expectation Propagation for Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBNs) describe structured stochastic...
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06/27/2012
Visualization of Collaborative Data
Collaborative data consist of ratings relating two distinct sets of obje...
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05/09/2012
Learning Continuous-Time Social Network Dynamics
We demonstrate that a number of sociology models for social network dyna...
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02/14/2012
Factored Filtering of Continuous-Time Systems
We consider filtering for a continuous-time, or asynchronous, stochastic...
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04/20/2002