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Hyper-parameter optimization based on soft actor critic and hierarchical mixture regularization
Hyper-parameter optimization is a crucial problem in machine learning as it aims to achieve the state-of-the-art performance in any model.
Hyper-parameter estimation method with particle swarm optimization
Bayesian optimization (BO) framework is capable of converting the optimization of the hyper-parameters into the optimization of an acquisition function.
Parallel ensemble methods for causal direction inference
Inferring the causal direction between two variables from their observation data is one of the most fundamental and challenging topics in data science.
Attacker and Defender Counting Approach for Abstract Argumentation
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.
Argument Ranking with Categoriser Function
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.
