Real-world data often exhibits class-imbalanced distributions, where a few classes (a.k.a. majority classes) occupy most instances and lots of classes (a.k.a. minority classes) have few instances. Neural classification models usually perform poorly on minority classes when training on such imbalanced datasets. To improve the performance on minority classes, existing methods typically re-balance the data distribution at the class level, i.e., assigning higher weights to minority classes and lower weights to majority classes during the training process. However, we observe that even the majority classes contain difficult instances to learn. By reducing the weights of the majority classes, such instances would become more difficult to learn and hurt the overall performance consequently. To tackle this problem, we propose a novel instance-level re-balancing strategy, which dynamically adjusts the sampling probabilities of instances according to the instance difficulty. Here the instance difficulty is measured based on the learning speed of instance, which is inspired by the human-leaning process (i.e., easier instances will be learned faster). We theoretically prove the correctness and convergence of our resampling algorithm. Empirical experiments demonstrate that our method significantly outperforms state-of-the-art re-balancing methods on the class-imbalanced datasets.

Policy gradient, which makes use of Monte Carlo method to get an unbiased estimation of the parameter gradients, has been widely used in reinforcement learning. One key issue in policy gradient is reducing the variance of the estimation. From the viewpoint of statistics, policy gradient with baseline, a successful variance reduction method for policy gradient, directly applies the control variates method, a traditional variance reduction technique used in Monte Carlo, to policy gradient. One problem with control variates method is that the quality of estimation heavily depends on the choice of the control variates. To address the issue and inspired by the antithetic variates method for variance reduction, we propose to combine the antithetic variates method with traditional policy gradient for the multi-armed bandit problem. Furthermore, we achieve a new policy gradient algorithm called Antithetic-Arm Bandit (AAB). In AAB, the gradient is estimated through coordinate ascent where at each iteration gradient of the target arm is estimated through: 1) constructing a sequence of arms which is approximately monotonic in terms of estimated gradients, 2) sampling a pair of antithetic arms over the sequence, and 3) re-estimating the target gradient based on the sampled pair. Theoretical analysis proved that AAB achieved an unbiased and variance reduced estimation. Experimental results based on a multi-armed bandit task showed that AAB can achieve state-of-the-art performances.