Policy evaluation is a critical step in the approximate solution of large Markov decision processes (MDPs), typically requiring O(|S|3 ) to directly solve the Bellman system of |S...
We consider support vector machines for binary classification. As opposed to most approaches we use the number of support vectors (the "L0 norm") as a regularizing term ...
Whenever a programmer writes a loop, or a mathematician does a proof by induction, an invariant is involved. The discovery and understanding of invariants often underlies problem s...
Abstract. Constraint satisfaction and propositional satisfiability problems are often solved using backtracking search. Previous studies have shown that a technique called randomi...
L1 regularization is effective for feature selection, but the resulting optimization is challenging due to the non-differentiability of the 1-norm. In this paper we compare state...