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APPROX
2005
Springer
111views Algorithms» more  APPROX 2005»
15 years 5 months ago
Sampling Bounds for Stochastic Optimization
A large class of stochastic optimization problems can be modeled as minimizing an objective function f that depends on a choice of a vector x ∈ X, as well as on a random external...
Moses Charikar, Chandra Chekuri, Martin Pál
ECML
2004
Springer
15 years 5 months ago
Batch Reinforcement Learning with State Importance
Abstract. We investigate the problem of using function approximation in reinforcement learning where the agent’s policy is represented as a classifier mapping states to actions....
Lihong Li, Vadim Bulitko, Russell Greiner
EAGC
2003
Springer
15 years 5 months ago
Mobile Work Environment for Grid Users
In the paper we aim to describe the project developing the Migrating Desktop infrastructure for the mobile users in a simple way. We also focus on the development testbed, which i...
Miroslaw Kupczyk, Rafal Lichwala, Norbert Meyer, B...
CIKM
2009
Springer
15 years 4 months ago
Heterogeneous cross domain ranking in latent space
Traditional ranking mainly focuses on one type of data source, and effective modeling still relies on a sufficiently large number of labeled or supervised examples. However, in m...
Bo Wang, Jie Tang, Wei Fan, Songcan Chen, Zi Yang,...
COLT
2006
Springer
15 years 3 months ago
Efficient Learning Algorithms Yield Circuit Lower Bounds
We describe a new approach for understanding the difficulty of designing efficient learning algorithms. We prove that the existence of an efficient learning algorithm for a circui...
Lance Fortnow, Adam R. Klivans