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» Online Gradient Descent Learning Algorithms
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FOCM
2008
140views more  FOCM 2008»
9 years 2 months ago
Online Gradient Descent Learning Algorithms
This paper considers the least-square online gradient descent algorithm in a reproducing kernel Hilbert space (RKHS) without explicit regularization. We present a novel capacity i...
Yiming Ying, Massimiliano Pontil
CORR
2004
Springer
103views Education» more  CORR 2004»
9 years 2 months ago
Online convex optimization in the bandit setting: gradient descent without a gradient
We study a general online convex optimization problem. We have a convex set S and an unknown sequence of cost functions c1, c2, . . . , and in each period, we choose a feasible po...
Abraham Flaxman, Adam Tauman Kalai, H. Brendan McM...
WSDM
2016
ACM
43views Data Mining» more  WSDM 2016»
3 years 10 months ago
Multileave Gradient Descent for Fast Online Learning to Rank
Modern search systems are based on dozens or even hundreds of ranking features. The dueling bandit gradient descent (DBGD) algorithm has been shown to effectively learn combinatio...
Anne Schuth, Harrie Oosterhuis, Shimon Whiteson, M...
ECIR
2016
Springer
3 years 10 months ago
Probabilistic Multileave Gradient Descent
Online learning to rank methods aim to optimize ranking models based on user interactions. The dueling bandit gradient descent (DBGD) algorithm is able to effectively optimize line...
Harrie Oosterhuis, Anne Schuth, Maarten de Rijke
COLT
2010
Springer
9 years 11 days ago
Composite Objective Mirror Descent
We present a new method for regularized convex optimization and analyze it under both online and stochastic optimization settings. In addition to unifying previously known firstor...
John Duchi, Shai Shalev-Shwartz, Yoram Singer, Amb...
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