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» Optimization on a Budget: A Reinforcement Learning Approach
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NIPS
1998
13 years 6 months ago
Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms
In this paper, we address two issues of long-standing interest in the reinforcement learning literature. First, what kinds of performance guarantees can be made for Q-learning aft...
Michael J. Kearns, Satinder P. Singh
PLDI
2005
ACM
13 years 10 months ago
Programming by sketching for bit-streaming programs
This paper introduces the concept of programming with sketches, an approach for the rapid development of high-performance applications. This approach allows a programmer to write ...
Armando Solar-Lezama, Rodric M. Rabbah, Rastislav ...
PEPM
2011
ACM
12 years 7 months ago
Adaptation-based programming in java
Writing deterministic programs is often difficult for problems whose optimal solutions depend on unpredictable properties of the programs’ inputs. Difficulty is also encounter...
Tim Bauer, Martin Erwig, Alan Fern, Jervis Pinto
JMLR
2006
124views more  JMLR 2006»
13 years 4 months ago
Policy Gradient in Continuous Time
Policy search is a method for approximately solving an optimal control problem by performing a parametric optimization search in a given class of parameterized policies. In order ...
Rémi Munos
VLDB
2007
ACM
137views Database» more  VLDB 2007»
13 years 10 months ago
Detecting Attribute Dependencies from Query Feedback
Real-world datasets exhibit a complex dependency structure among the data attributes. Learning this structure is a key task in automatic statistics configuration for query optimi...
Peter J. Haas, Fabian Hueske, Volker Markl