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» From Optimization to Regret Minimization and Back Again
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ALT
2010
Springer
13 years 6 months ago
Optimal Online Prediction in Adversarial Environments
: In many prediction problems, including those that arise in computer security and computational finance, the process generating the data is best modeled as an adversary with whom ...
Peter L. Bartlett
NIPS
2007
13 years 6 months ago
The Price of Bandit Information for Online Optimization
In the online linear optimization problem, a learner must choose, in each round, a decision from a set D ⊂ Rn in order to minimize an (unknown and changing) linear cost function...
Varsha Dani, Thomas P. Hayes, Sham Kakade
CVPR
2010
IEEE
14 years 1 months ago
Ink-Bleed Reduction using Functional Minimization
Ink-bleed interference is undesirable as it reduces the legibility and aesthetics of affected documents. We present a novel approach to reduce ink-bleed interference using functio...
Grani Adiwena Hanasusanto, Michael Brown, Zheng Wu
ECCC
2007
180views more  ECCC 2007»
13 years 4 months ago
Adaptive Algorithms for Online Decision Problems
We study the notion of learning in an oblivious changing environment. Existing online learning algorithms which minimize regret are shown to converge to the average of all locally...
Elad Hazan, C. Seshadhri
CORR
2010
Springer
171views Education» more  CORR 2010»
12 years 11 months ago
Online Learning in Opportunistic Spectrum Access: A Restless Bandit Approach
We consider an opportunistic spectrum access (OSA) problem where the time-varying condition of each channel (e.g., as a result of random fading or certain primary users' activ...
Cem Tekin, Mingyan Liu