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CORR
2010
Springer
116views Education» more  CORR 2010»
13 years 5 months ago
Adaptive Bound Optimization for Online Convex Optimization
We introduce a new online convex optimization algorithm that adaptively chooses its regularization function based on the loss functions observed so far. This is in contrast to pre...
H. Brendan McMahan, Matthew J. Streeter
CORR
2004
Springer
103views Education» more  CORR 2004»
13 years 5 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...
NIPS
2007
13 years 6 months ago
Adaptive Online Gradient Descent
We study the rates of growth of the regret in online convex optimization. First, we show that a simple extension of the algorithm of Hazan et al eliminates the need for a priori k...
Peter L. Bartlett, Elad Hazan, Alexander Rakhlin
COLT
2010
Springer
13 years 3 months ago
Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback
Bandit convex optimization is a special case of online convex optimization with partial information. In this setting, a player attempts to minimize a sequence of adversarially gen...
Alekh Agarwal, Ofer Dekel, Lin Xiao
NIPS
2004
13 years 6 months ago
Nearly Tight Bounds for the Continuum-Armed Bandit Problem
In the multi-armed bandit problem, an online algorithm must choose from a set of strategies in a sequence of n trials so as to minimize the total cost of the chosen strategies. Wh...
Robert D. Kleinberg