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» Tight lower bounds for the online labeling problem
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SPAA
2004
ACM
14 years 1 months ago
Online algorithms for prefetching and caching on parallel disks
Parallel disks provide a cost effective way of speeding up I/Os in applications that work with large amounts of data. The main challenge is to achieve as much parallelism as poss...
Rahul Shah, Peter J. Varman, Jeffrey Scott Vitter
COLT
2010
Springer
13 years 5 months ago
Regret Minimization With Concept Drift
In standard online learning, the goal of the learner is to maintain an average loss that is "not too big" compared to the loss of the best-performing function in a fixed...
Koby Crammer, Yishay Mansour, Eyal Even-Dar, Jenni...
APPROX
2010
Springer
138views Algorithms» more  APPROX 2010»
13 years 9 months ago
Maximum Flows on Disjoint Paths
We consider the question: What is the maximum flow achievable in a network if the flow must be decomposable into a collection of edgedisjoint paths? Equivalently, we wish to find a...
Guyslain Naves, Nicolas Sonnerat, Adrian Vetta
COCO
2004
Springer
78views Algorithms» more  COCO 2004»
14 years 1 months ago
Language Compression and Pseudorandom Generators
The language compression problem asks for succinct descriptions of the strings in a language A such that the strings can be efficiently recovered from their description when given...
Harry Buhrman, Troy Lee, Dieter van Melkebeek
NIPS
2004
13 years 9 months ago
Analysis of a greedy active learning strategy
act out the core search problem of active learning schemes, to better understand the extent to which adaptive labeling can improve sample complexity. We give various upper and low...
Sanjoy Dasgupta