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SIAMJO
2008
85views more  SIAMJO 2008»
15 years 6 months ago
Explicit Reformulations for Robust Optimization Problems with General Uncertainty Sets
We consider a rather general class of mathematical programming problems with data uncertainty, where the uncertainty set is represented by a system of convex inequalities. We prove...
Igor Averbakh, Yun-Bin Zhao
SIAMJO
2010
127views more  SIAMJO 2010»
15 years 1 months ago
Trace Norm Regularization: Reformulations, Algorithms, and Multi-Task Learning
We consider a recently proposed optimization formulation of multi-task learning based on trace norm regularized least squares. While this problem may be formulated as a semidefini...
Ting Kei Pong, Paul Tseng, Shuiwang Ji, Jieping Ye
ICALP
2011
Springer
14 years 10 months ago
On the Advice Complexity of the k-Server Problem
Competitive analysis is the established tool for measuring the output quality of algorithms that work in an online environment. Recently, the model of advice complexity has been in...
Hans-Joachim Böckenhauer, Dennis Komm, Rastis...
JMLR
2012
13 years 9 months ago
Marginal Regression For Multitask Learning
Variable selection is an important and practical problem that arises in analysis of many high-dimensional datasets. Convex optimization procedures that arise from relaxing the NP-...
Mladen Kolar, Han Liu
PPOPP
2010
ACM
16 years 4 months ago
Featherweight X10: a core calculus for async-finish parallelism
We present a core calculus with two of X10's key constructs for parallelism, namely async and finish. Our calculus forms a convenient basis for type systems and static analys...
Jonathan K. Lee, Jens Palsberg