This paper presents the result for Simultaneous Perturbation Stochastic Approximation (SPSA) on the BBOB 2010 noisy testbed. SPSA is a stochastic gradient approximation strategy w...
Markov decision processes (MDPs) with discrete and continuous state and action components can be solved efficiently by hybrid approximate linear programming (HALP). The main idea ...
An autonomous variational inference algorithm for arbitrary graphical models requires the ability to optimize variational approximations over the space of model parameters as well...
Depth-first branch-and-bound (DFBnB) is a complete algorithm that is typically used to find optimal solutions of difficult combinatorial optimization problems. It can also be adap...
We propose an algorithm for function approximation that evolves a set of hierarchical piece-wise linear regressors. The algorithm, named HIRE-Lin, follows the iterative rule learn...