Achievability results for statistical learning under communication constraints
Achievability results for statistical learning under communication constraints
复制标题
通信限制下统计学习的可实现性结果
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
M. Raginsky
中科院分区：
文献类型：
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作者：
M. Raginsky
统计学习的问题是在独立同分布的基础上构造一个随机变量作为相关随机变量的函数的精确预测器。从他们的联合分布训练样本。允许的预测器被限制在某个指定的类中，目标是渐近地接近该类中最佳预测器的性能。我们考虑两种设置中，学习代理只能访问速率受限的描述的训练数据，并提出信息理论界的预测性能，可实现在这些通信约束的存在。我们的证明不假设压缩和学习之间的任何分离结构，并依赖于一类新的操作标准，专门针对速率约束设置中的编码器和学习算法的联合设计。
The problem of statistical learning is to construct an accurate predictor of a random variable as a function of a correlated random variable on the basis of an i.i.d. training sample from their joint distribution. Allowable predictors are constrained to lie in some specified class, and the goal is to approach asymptotically the performance of the best predictor in the class. We consider two settings in which the learning agent only has access to rate-limited descriptions of the training data, and present information-theoretic bounds on the predictor performance achievable in the presence of these communication constraints. Our proofs do not assume any separation structure between compression and learning and rely on a new class of operational criteria specifically tailored to joint design of encoders and learning algorithms in rate-constrained settings.