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ICML
2005
IEEE
16 years 7 months ago
Proto-value functions: developmental reinforcement learning
This paper presents a novel framework called proto-reinforcement learning (PRL), based on a mathematical model of a proto-value function: these are task-independent basis function...
Sridhar Mahadevan
ALT
2006
Springer
16 years 3 months ago
Risk-Sensitive Online Learning
We consider the problem of online learning in settings in which we want to compete not simply with the rewards of the best expert or stock, but with the best trade-off between rew...
Eyal Even-Dar, Michael J. Kearns, Jennifer Wortman
EUROCOLT
1995
Springer
15 years 10 months ago
The structure of intrinsic complexity of learning
Limiting identification of r.e. indexes for r.e. languages (from a presentation of elements of the language) and limiting identification of programs for computable functions (fr...
Sanjay Jain, Arun Sharma
ICML
2010
IEEE
15 years 7 months ago
Learning Efficiently with Approximate Inference via Dual Losses
Many structured prediction tasks involve complex models where inference is computationally intractable, but where it can be well approximated using a linear programming relaxation...
Ofer Meshi, David Sontag, Tommi Jaakkola, Amir Glo...
ICML
2007
IEEE
16 years 7 months ago
Learning state-action basis functions for hierarchical MDPs
This paper introduces a new approach to actionvalue function approximation by learning basis functions from a spectral decomposition of the state-action manifold. This paper exten...
Sarah Osentoski, Sridhar Mahadevan