Reinforcement learning algorithms can become unstable when combined with linear function approximation. Algorithms that minimize the mean-square Bellman error are guaranteed to co...
In this paper, we derive lower and upper bounds for the probability of error for a linear classifier, where the random vectors representing the underlying classes obey the multivar...
We design a linear time approximation scheme for the GaleBerlekamp Switching Game and generalize it to a wider class of dense fragile minimization problems including the Nearest C...
We consider the problem known as MAX-SATISFY: given a system of m linear equations over the rationals, find a maximum set of equations that can be satisfied. Let r be the width of...
In this paper, we define the notion of approximate bisimulation relation between two continuous systems. While exact bisimulation requires that the observations of two systems ar...