APAC: Authorized Probability-controlled Actor-Critic For Offline Reinforcement Learning

Due to the inability to interact with the environment, offline reinforcement learning (RL) methods face the challenge of estimating the Out-of-Distribution (OOD) points. Most existing methods exclude the OOD areas or restrict the value of $Q$ function. However, these methods either are over-conservative or suffer from model uncertainty prediction. In this paper, we propose an authorized probabilistic-control policy learning (APAC) method. The proposed method learns the distribution characteristics of the feasible states/actions by utilizing the flow-GAN model. Specifically, APAC avoids taking action in the low probability density region of behavior policy, while allows exploration in the authorized high probability density region. Theoretical proofs are provided to justify the advantage of APAC. Empirically, APAC outperforms existing alternatives on a variety of simulated tasks, and yields higher expected returns.

Community Detection on Mixture Multi-layer Networks via Regularized Tensor Decomposition

We study the problem of community detection in multi-layer networks, where pairs of nodes can be related in multiple modalities. We introduce a general framework, i.e., mixture multi-layer stochastic block model (MMSBM), which includes many earlier models as special cases. We propose a tensor-based algorithm (TWIST) to reveal both global/local memberships of nodes, and memberships of layers. We show that the TWIST procedure can accurately detect the communities with small misclassification error as the number of nodes and/or the number of layers increases. Numerical studies confirm our theoretical findings. To our best knowledge, this is the first systematic study on the mixture multi-layer networks using tensor decomposition. The method is applied to two real datasets: worldwide trading networks and malaria parasite genes networks, yielding new and interesting findings.

Regularized maximum correntropy machine

In this paper we investigate the usage of regularized correntropy framework for learning of classifiers from noisy labels. The class label predictors learned by minimizing transitional loss functions are sensitive to the noisy and outlying labels of training samples, because the transitional loss functions are equally applied to all the samples. To solve this problem, we propose to learn the class label predictors by maximizing the correntropy between the predicted labels and the true labels of the training samples, under the regularized Maximum Correntropy Criteria (MCC) framework. Moreover, we regularize the predictor parameter to control the complexity of the predictor. The learning problem is formulated by an objective function considering the parameter regularization and MCC simultaneously. By optimizing the objective function alternately, we develop a novel predictor learning algorithm. The experiments on two chal- lenging pattern classification tasks show that it significantly outperforms the machines with transitional loss functions.