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JAIR
2006
160views more  JAIR 2006»
13 years 7 months ago
Anytime Point-Based Approximations for Large POMDPs
The Partially Observable Markov Decision Process has long been recognized as a rich framework for real-world planning and control problems, especially in robotics. However exact s...
Joelle Pineau, Geoffrey J. Gordon, Sebastian Thrun
GECCO
2007
Springer
161views Optimization» more  GECCO 2007»
14 years 1 months ago
Alternative techniques to solve hard multi-objective optimization problems
In this paper, we propose the combination of different optimization techniques in order to solve “hard” two- and threeobjective optimization problems at a relatively low comp...
Ricardo Landa Becerra, Carlos A. Coello Coello, Al...
RANDOM
2001
Springer
14 years 1 days ago
On the Equivalence between the Primal-Dual Schema and the Local-Ratio Technique
We discuss two approximation approaches, the primal-dual schema and the local-ratio technique. We present two relatively simple frameworks, one for each approach, which extend know...
Reuven Bar-Yehuda, Dror Rawitz
JMLR
2010
145views more  JMLR 2010»
13 years 2 months ago
Kernel Partial Least Squares is Universally Consistent
We prove the statistical consistency of kernel Partial Least Squares Regression applied to a bounded regression learning problem on a reproducing kernel Hilbert space. Partial Lea...
Gilles Blanchard, Nicole Krämer
ATAL
2010
Springer
13 years 8 months ago
Risk-sensitive planning in partially observable environments
Partially Observable Markov Decision Process (POMDP) is a popular framework for planning under uncertainty in partially observable domains. Yet, the POMDP model is riskneutral in ...
Janusz Marecki, Pradeep Varakantham