Yulai Zhang

Yaru Li, Yulai Zhang

Particle swarm optimization (PSO) method cannot be directly used in the problem of hyper-parameter estimation since the mathematical formulation of the mapping from hyper-parameters to loss function or generalization accuracy is unclear. Bayesian optimization (BO) framework is capable of converting the optimization of the hyper-parameters into the optimization of an acquisition function. The acquisition function is non-convex and multi-peak. So the problem can be better solved by the PSO. The proposed method in this paper uses the particle swarm method to optimize the acquisition function in the BO framework to get better hyper-parameters. The performances of proposed method in both of the classification and regression models are evaluated and demonstrated. The results on several benchmark problems are improved.

Fuan Pu, Jian Luo, Yulai Zhang et al.

In Dung's abstract argumentation, arguments are either acceptable or unacceptable, given a chosen notion of acceptability. This gives a coarse way to compare arguments. In this paper, we propose a counting approach for a more fine-gained assessment to arguments by counting the number of their respective attackers and defenders based on argument graph and argument game. An argument is more acceptable if the proponent puts forward more number of defenders for it and the opponent puts forward less number of attackers against it. We show that our counting model has two well-behaved properties: normalization and convergence. Then, we define a counting semantics based on this model, and investigate some general properties of the semantics.

Fuan Pu, Jian Luo, Yulai Zhang et al.

Recently, ranking-based semantics is proposed to rank-order arguments from the most acceptable to the weakest one(s), which provides a graded assessment to arguments. In general, the ranking on arguments is derived from the strength values of the arguments. Categoriser function is a common approach that assigns a strength value to a tree of arguments. When it encounters an argument system with cycles, then the categoriser strength is the solution of the non-linear equations. However, there is no detail about the existence and uniqueness of the solution, and how to find the solution (if exists). In this paper, we will cope with these issues via fixed point technique. In addition, we define the categoriser-based ranking semantics in light of categoriser strength, and investigate some general properties of it. Finally, the semantics is shown to satisfy some of the axioms that a ranking-based semantics should satisfy.