Probabilistic decision graphs (PDGs) are a representation language for probability distributions based on binary decision diagrams. PDGs can encode (context-specific) independence...
Most machine learning algorithms are lazy: they extract from the training set the minimum information needed to predict its labels. Unfortunately, this often leads to models that ...
Joseph O'Sullivan, John Langford, Rich Caruana, Av...
The typical constraint store transmits a limited amount of information because it consists only of variable domains. We propose a richer constraint store in the form of a limited-w...
Henrik Reif Andersen, Tarik Hadzic, John N. Hooker...
Reinforcement learning is an effective technique for learning action policies in discrete stochastic environments, but its efficiency can decay exponentially with the size of the ...
The Kushilevitz-Mansour (KM) algorithm is an algorithm that finds all the “large” Fourier coefficients of a Boolean function. It is the main tool for learning decision trees ...