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ICML
2008
IEEE
16 years 7 months ago
Apprenticeship learning using linear programming
In apprenticeship learning, the goal is to learn a policy in a Markov decision process that is at least as good as a policy demonstrated by an expert. The difficulty arises in tha...
Umar Syed, Michael H. Bowling, Robert E. Schapire
ICML
2006
IEEE
16 years 7 months ago
Autonomous shaping: knowledge transfer in reinforcement learning
We introduce the use of learned shaping rewards in reinforcement learning tasks, where an agent uses prior experience on a sequence of tasks to learn a portable predictor that est...
George Konidaris, Andrew G. Barto
ICML
2004
IEEE
16 years 7 months ago
Support vector machine learning for interdependent and structured output spaces
Learning general functional dependencies is one of the main goals in machine learning. Recent progress in kernel-based methods has focused on designing flexible and powerful input...
Ioannis Tsochantaridis, Thomas Hofmann, Thorsten J...
ICML
2009
IEEE
16 years 1 months ago
Learning linear dynamical systems without sequence information
Virtually all methods of learning dynamic systems from data start from the same basic assumption: that the learning algorithm will be provided with a sequence, or trajectory, of d...
Tzu-Kuo Huang, Jeff Schneider
ICRA
2009
IEEE
145views Robotics» more  ICRA 2009»
16 years 29 days ago
Learning 3-D object orientation from images
— We propose a learning algorithm for estimating the 3-D orientation of objects. Orientation learning is a difficult problem because the space of orientations is non-Euclidean, ...
Ashutosh Saxena, Justin Driemeyer, Andrew Y. Ng