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WSC
1997
13 years 8 months ago
How Common Random Numbers Affect Multinomial Selection
We consider the effect of common random numbers (CRNs) among simulated systems in two different multinomial selection procedures; the classical procedure BEM (Bechhofer, Elmaghr...
John O. Miller, Kenneth W. Bauer Jr.
ESA
2010
Springer
227views Algorithms» more  ESA 2010»
13 years 8 months ago
Approximating Parameterized Convex Optimization Problems
We consider parameterized convex optimization problems over the unit simplex, that depend on one parameter. We provide a simple and efficient scheme for maintaining an -approximat...
Joachim Giesen, Martin Jaggi, Sören Laue
STACS
1999
Springer
13 years 11 months ago
A Complete and Tight Average-Case Analysis of Learning Monomials
Abstract. We advocate to analyze the average complexity of learning problems. An appropriate framework for this purpose is introduced. Based on it we consider the problem of learni...
Rüdiger Reischuk, Thomas Zeugmann
TIT
1998
70views more  TIT 1998»
13 years 7 months ago
The Importance of Convexity in Learning with Squared Loss
We show that if the closureof a function class F under the metric induced by some probability distribution is not convex, then the sample complexity for agnostically learning F wi...
Wee Sun Lee, Peter L. Bartlett, Robert C. Williams...
ML
2012
ACM
413views Machine Learning» more  ML 2012»
12 years 3 months ago
Gradient-based boosting for statistical relational learning: The relational dependency network case
Dependency networks approximate a joint probability distribution over multiple random variables as a product of conditional distributions. Relational Dependency Networks (RDNs) are...
Sriraam Natarajan, Tushar Khot, Kristian Kersting,...