论文标题
MBGDT：强大的迷你批次梯度下降
MBGDT:Robust Mini-Batch Gradient Descent
论文作者
论文摘要
在高维度中，大多数机器学习方法即使有一些异常值也要脆弱。为了解决这个问题，我们希望与基础学习者一起引入一种新方法，例如贝叶斯回归或随机梯度下降，以解决模型中脆弱性的问题。由于迷你批次梯度下降允许比批处理梯度下降具有更强的收敛性，因此我们使用Mini Batch梯度下降（称为Mini Batch梯度下降）使用Trimming（MBGDT）来使用一种方法。当我们在设计数据集中应用方法时，我们的方法显示出最先进的性能，并且比几个基线具有更大的鲁棒性。
In high dimensions, most machine learning method perform fragile even there are a little outliers. To address this, we hope to introduce a new method with the base learner, such as Bayesian regression or stochastic gradient descent to solve the problem of the vulnerability in the model. Because the mini-batch gradient descent allows for a more robust convergence than the batch gradient descent, we work a method with the mini-batch gradient descent, called Mini-Batch Gradient Descent with Trimming (MBGDT). Our method show state-of-art performance and have greater robustness than several baselines when we apply our method in designed dataset.
