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» On the Convergence of Bound Optimization Algorithms
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ML
2002
ACM
121views Machine Learning» more  ML 2002»
15 years 6 months ago
Near-Optimal Reinforcement Learning in Polynomial Time
We present new algorithms for reinforcement learning, and prove that they have polynomial bounds on the resources required to achieve near-optimal return in general Markov decisio...
Michael J. Kearns, Satinder P. Singh
GECCO
2008
Springer
153views Optimization» more  GECCO 2008»
15 years 7 months ago
G-Metric: an M-ary quality indicator for the evaluation of non-dominated sets
An open problem in multiobjective optimization using the Pareto optimality criteria, is how to evaluate the performance of different evolutionary algorithms that solve multi– o...
Giovanni Lizárraga Lizárraga, Arturo...
CDC
2010
IEEE
160views Control Systems» more  CDC 2010»
15 years 1 months ago
Aggregation-based model reduction of a Hidden Markov Model
This paper is concerned with developing an information-theoretic framework to aggregate the state space of a Hidden Markov Model (HMM) on discrete state and observation spaces. The...
Kun Deng, Prashant G. Mehta, Sean P. Meyn
PPSN
2010
Springer
15 years 4 months ago
Tight Bounds for the Approximation Ratio of the Hypervolume Indicator
The hypervolume indicator is widely used to guide the search and to evaluate the performance of evolutionary multi-objective optimization algorithms. It measures the volume of the ...
Karl Bringmann, Tobias Friedrich
SAGT
2009
Springer
155views Game Theory» more  SAGT 2009»
16 years 29 days ago
Anarchy, Stability, and Utopia: Creating Better Matchings
We consider the loss in social welfare caused by individual rationality in matching scenarios. We give both theoretical and experimental results comparing stable matchings with soc...
Elliot Anshelevich, Sanmay Das, Yonatan Naamad