Less Regret via Online Conditioning
Less Regret via Online Conditioning
复制标题
通过在线调理减少遗憾
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
H. B. McMahan
中科院分区：
文献类型：
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作者：
Matthew J. Streeter;H. B. McMahan
我们通过适应性的学习率调整来分析和评估在线梯度下降算法。可以将我们的算法视为具有对角预调节器的在线版本的批处理梯度下降。这种方法导致遗憾的界限比标准在线梯度下降的范围更强，以解决一般在线凸优化问题。在实验上，我们表明我们的算法具有与最先进的算法有关大规模机器学习问题的竞争。
We analyze and evaluate an online gradient descent algorithm with adaptive per-coordinate adjustment of learning rates. Our algorithm can be thought of as an online version of batch gradient descent with a diagonal preconditioner. This approach leads to regret bounds that are stronger than those of standard online gradient descent for general online convex optimization problems. Experimentally, we show that our algorithm is competitive with state-of-the-art algorithms for large scale machine learning problems.