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APPROX
2005
Springer
111views Algorithms» more  APPROX 2005»
15 years 11 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 11 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
178
Voted
EAGC
2003
Springer
15 years 11 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 10 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,...
174
Voted
COLT
2006
Springer
15 years 9 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