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Publication
240views
12 years 6 months ago
Bayesian multitask inverse reinforcement learning
We generalise the problem of inverse reinforcement learning to multiple tasks, from multiple demonstrations. Each one may represent one expert trying to solve a different task, or ...
Christos Dimitrakakis, Constantin A. Rothkopf
AI
2005
Springer
13 years 7 months ago
Unsupervised named-entity extraction from the Web: An experimental study
The KNOWITALL system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an unsupervised, doma...
Oren Etzioni, Michael J. Cafarella, Doug Downey, A...
NIPS
2007
13 years 9 months ago
Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion
We consider apprenticeship learning—learning from expert demonstrations—in the setting of large, complex domains. Past work in apprenticeship learning requires that the expert...
J. Zico Kolter, Pieter Abbeel, Andrew Y. Ng
MM
2009
ACM
277views Multimedia» more  MM 2009»
14 years 2 months ago
Inferring semantic concepts from community-contributed images and noisy tags
In this paper, we exploit the problem of inferring images’ semantic concepts from community-contributed images and their associated noisy tags. To infer the concepts more accura...
Jinhui Tang, Shuicheng Yan, Richang Hong, Guo-Jun ...
ICRA
2009
IEEE
170views Robotics» more  ICRA 2009»
14 years 2 months ago
Imitation learning with generalized task descriptions
— In this paper, we present an approach that allows a robot to observe, generalize, and reproduce tasks observed from multiple demonstrations. Motion capture data is recorded in ...
Clemens Eppner, Jürgen Sturm, Maren Bennewitz...