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» Improved Risk Tail Bounds for On-Line Algorithms
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PODS
2009
ACM
100views Database» more  PODS 2009»
14 years 7 months ago
Space-optimal heavy hitters with strong error bounds
The problem of finding heavy hitters and approximating the frequencies of items is at the heart of many problems in data stream analysis. It has been observed that several propose...
Radu Berinde, Graham Cormode, Piotr Indyk, Martin ...
AAAI
2008
13 years 9 months ago
An Efficient Motion Planning Algorithm for Stochastic Dynamic Systems with Constraints on Probability of Failure
When controlling dynamic systems, such as mobile robots in uncertain environments, there is a trade off between risk and reward. For example, a race car can turn a corner faster b...
Masahiro Ono, Brian C. Williams
CORR
2011
Springer
202views Education» more  CORR 2011»
13 years 2 months ago
Online Least Squares Estimation with Self-Normalized Processes: An Application to Bandit Problems
The analysis of online least squares estimation is at the heart of many stochastic sequential decision-making problems. We employ tools from the self-normalized processes to provi...
Yasin Abbasi-Yadkori, Dávid Pál, Csa...
ICNS
2007
IEEE
14 years 1 months ago
DRR-SFF: A Practical Scheduling Algorithm to Improve the Performance of Short Flows
— Short flow first scheduling (SFF) strategy is effective in obtaining more stringent performance bounds for short flows in Internet. However, previous strict SFF approaches i...
Changhua Sun, Lei Shi, Chengchen Hu, Bin Liu
COLT
2006
Springer
13 years 11 months ago
A Randomized Online Learning Algorithm for Better Variance Control
We propose a sequential randomized algorithm, which at each step concentrates on functions having both low risk and low variance with respect to the previous step prediction functi...
Jean-Yves Audibert