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» On learning with dissimilarity functions
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IROS
2009
IEEE
150views Robotics» more  IROS 2009»
16 years 21 days ago
Learning locomotion over rough terrain using terrain templates
— We address the problem of foothold selection in robotic legged locomotion over very rough terrain. The difficulty of the problem we address here is comparable to that of human...
Mrinal Kalakrishnan, Jonas Buchli, Peter Pastor, S...
SIGIR
2006
ACM
16 years 4 hour ago
Learning to advertise
Content-targeted advertising, the task of automatically associating ads to a Web page, constitutes a key Web monetization strategy nowadays. Further, it introduces new challenging...
Anísio Lacerda, Marco Cristo, Marcos Andr&e...
NIPS
2001
15 years 7 months ago
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning
Policy gradient methods for reinforcement learning avoid some of the undesirable properties of the value function approaches, such as policy degradation (Baxter and Bartlett, 2001...
Evan Greensmith, Peter L. Bartlett, Jonathan Baxte...
202
Voted
NN
2000
Springer
192views Neural Networks» more  NN 2000»
15 years 5 months ago
A new algorithm for learning in piecewise-linear neural networks
Piecewise-linear (PWL) neural networks are widely known for their amenability to digital implementation. This paper presents a new algorithm for learning in PWL networks consistin...
Emad Gad, Amir F. Atiya, Samir I. Shaheen, Ayman E...
ALT
2010
Springer
15 years 7 months ago
Distribution-Dependent PAC-Bayes Priors
We further develop the idea that the PAC-Bayes prior can be informed by the data-generating distribution. We prove sharp bounds for an existing framework of Gibbs algorithms, and ...
Guy Lever, François Laviolette, John Shawe-...