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JAIR
2006
160views more  JAIR 2006»
15 years 4 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»
15 years 10 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
15 years 8 months 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»
14 years 11 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
15 years 5 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