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ECML
2004
Springer
15 years 10 months ago
Batch Reinforcement Learning with State Importance
Abstract. We investigate the problem of using function approximation in reinforcement learning where the agent’s policy is represented as a classifier mapping states to actions....
Lihong Li, Vadim Bulitko, Russell Greiner
STOC
1993
ACM
117views Algorithms» more  STOC 1993»
15 years 8 months ago
Efficient noise-tolerant learning from statistical queries
In this paper, we study the problem of learning in the presence of classification noise in the probabilistic learning model of Valiant and its variants. In order to identify the cl...
Michael J. Kearns
CORR
2006
Springer
99views Education» more  CORR 2006»
15 years 4 months ago
Logical settings for concept learning from incomplete examples in First Order Logic
We investigate here concept learning from incomplete examples. Our first purpose is to discuss to what extent logical learning settings have to be modified in order to cope with da...
Dominique Bouthinon, Henry Soldano, Véroniq...
COST
2009
Springer
185views Multimedia» more  COST 2009»
15 years 2 months ago
How an Agent Can Detect and Use Synchrony Parameter of Its Own Interaction with a Human?
Synchrony is claimed by psychology as a crucial parameter of any social interaction: to give to human a feeling of natural interaction, a feeling of agency [17], an agent must be a...
Ken Prepin, Philippe Gaussier
CVPR
2005
IEEE
16 years 6 months ago
Learning Appearance Manifolds from Video
The appearance of dynamic scenes is often largely governed by a latent low-dimensional dynamic process. We show how to learn a mapping from video frames to this lowdimensional rep...
Ali Rahimi, Ben Recht, Trevor Darrell