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AAAI
2008
15 years 4 days ago
Economic Hierarchical Q-Learning
Hierarchical state decompositions address the curse-ofdimensionality in Q-learning methods for reinforcement learning (RL) but can suffer from suboptimality. In addressing this, w...
Erik G. Schultink, Ruggiero Cavallo, David C. Park...
CEC
2007
IEEE
14 years 11 months ago
Fitness inheritance in evolutionary and multi-objective high-level synthesis
Abstract—The high-level synthesis process allows the automatic design and implementation of digital circuits starting from a behavioral description. Evolutionary algorithms are v...
Christian Pilato, Gianluca Palermo, Antonino Tumeo...
ESA
2008
Springer
136views Algorithms» more  ESA 2008»
14 years 11 months ago
Oblivious Randomized Direct Search for Real-Parameter Optimization
The focus is on black-box optimization of a function f : RN R given as a black box, i. e. an oracle for f-evaluations. This is commonly called direct search, and in fact, most meth...
Jens Jägersküpper
NIPS
2008
14 years 11 months ago
On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization
This work characterizes the generalization ability of algorithms whose predictions are linear in the input vector. To this end, we provide sharp bounds for Rademacher and Gaussian...
Sham M. Kakade, Karthik Sridharan, Ambuj Tewari
NIPS
2008
14 years 11 months ago
On the Efficient Minimization of Classification Calibrated Surrogates
Bartlett et al (2006) recently proved that a ground condition for convex surrogates, classification calibration, ties up the minimization of the surrogates and classification risk...
Richard Nock, Frank Nielsen