Efficiently learning linear-linear exponential family predictive representations of state

Proceedings of the 25th International Conference on Machine Learning

Volumn , Issue , 2008, Pages 1176-1183

  Wingate, David ^a   Singh, Satinder ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DYNAMICAL SYSTEMS; MACHINE LEARNING; MAXIMUM LIKELIHOOD; STOCHASTIC MODELS; STOCHASTIC SYSTEMS; EDUCATION; IMAGE SEGMENTATION; LEARNING SYSTEMS; ROBOT LEARNING;

COMPUTATIONALLY EFFICIENT; EXPONENTIAL FAMILY; EXPONENTIAL FAMILY DISTRIBUTIONS; FUTURE OBSERVATIONS; LIKELIHOOD FUNCTIONS; MAXIMUM LIKELIHOOD LEARNING; STATIONARY DISTRIBUTION; STOCHASTIC DYNAMICAL SYSTEM; EXPONENTIAL FAMILIES; NEW MODELS; STATIONARY DISTRIBUTIONS; STOCHASTIC DYNAMICAL SYSTEMS;

LEARNING ALGORITHMS;

EID: 56449115195     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390156.1390304     Document Type: Conference Paper

References (6)

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3. Littman, M. L., Sutton, R. S., & Singh, S. (2002). Predictive representations of state. Neural Information Processing Systems (NIPS) (pp. 1555-1561).
4. Peters, J., Vijayakumar, S., & Schaal, S. (2005). Natural actor-critic. European Conference on Machine Learning (ECML) (pp. 280-291).
5. Rudary, M. R., Singh, S., & Wingate, D. (2005). Predictive linear-Gaussian models of stochastic dynamical systems. Uncertainty in Artificial Intelligence (pp. 501-508).
6. Wingate, D. (2008). Exponential family predictive representations of state. PhD thesis, University of Michigan.