Moment matching is a popular means of parametric density estimation. We extend this technique to nonparametric estimation of mixture models. Our approach works by embedding distri...
In inductive logic programming, subsumption is a widely used coverage test. Unfortunately, testing -subsumption is NP-complete, which represents a crucial efficiency bottleneck fo...
Stationarity is often an unrealistic prior assumption for Gaussian process regression. One solution is to predefine an explicit nonstationary covariance function, but such covaria...
We introduce relational temporal difference learning as an effective approach to solving multi-agent Markov decision problems with large state spaces. Our algorithm uses temporal ...
A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the n...