论文标题
最佳预测程序的对抗蒙特卡洛元学习
Adversarial Monte Carlo Meta-Learning of Optimal Prediction Procedures
论文作者
论文摘要
我们将预测过程的元学习构图作为在两人游戏中寻找最佳策略的搜索。在这个游戏中，自然选择了先前的分布，该分布生成了由特征和相关结果组成的标记数据，并且预测变量观察了从此前从该先验中绘制的分布采样的数据。预测变量的目标是学习一个将新功能映射到相关结果估计的函数。我们确定，在合理条件下，预测变量具有最佳策略，该策略与结果的转移和重新分组相等，并且是对观测值以及特征的转移，重新缩放和置换的不变。我们介绍了满足这些属性的神经网络体系结构。在参数和非参数实验中，与标准实践相比，提出的策略表现出色。
We frame the meta-learning of prediction procedures as a search for an optimal strategy in a two-player game. In this game, Nature selects a prior over distributions that generate labeled data consisting of features and an associated outcome, and the Predictor observes data sampled from a distribution drawn from this prior. The Predictor's objective is to learn a function that maps from a new feature to an estimate of the associated outcome. We establish that, under reasonable conditions, the Predictor has an optimal strategy that is equivariant to shifts and rescalings of the outcome and is invariant to permutations of the observations and to shifts, rescalings, and permutations of the features. We introduce a neural network architecture that satisfies these properties. The proposed strategy performs favorably compared to standard practice in both parametric and nonparametric experiments.
