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» The complexity of stochastic sequences
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ML
2002
ACM
143views Machine Learning» more  ML 2002»
15 years 6 months ago
A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes
An issue that is critical for the application of Markov decision processes MDPs to realistic problems is how the complexity of planning scales with the size of the MDP. In stochas...
Michael J. Kearns, Yishay Mansour, Andrew Y. Ng
181
Voted
ICRA
2010
IEEE
145views Robotics» more  ICRA 2010»
15 years 5 months ago
Reinforcement learning of motor skills in high dimensions: A path integral approach
— Reinforcement learning (RL) is one of the most general approaches to learning control. Its applicability to complex motor systems, however, has been largely impossible so far d...
Evangelos Theodorou, Jonas Buchli, Stefan Schaal
MA
2010
Springer
132views Communications» more  MA 2010»
15 years 5 months ago
Model selection by sequentially normalized least squares
Model selection by the predictive least squares (PLS) principle has been thoroughly studied in the context of regression model selection and autoregressive (AR) model order estima...
Jorma Rissanen, Teemu Roos, Petri Myllymäki
ICCAD
2010
IEEE
117views Hardware» more  ICCAD 2010»
15 years 4 months ago
A synthesis flow for digital signal processing with biomolecular reactions
Abstract--We present a methodology for implementing digital signal processing (DSP) operations such as filtering with biomolecular reactions. From a DSP specification, we demonstra...
Hua Jiang, Aleksandra P. Kharam, Marc D. Riedel, K...
ICRA
2010
IEEE
185views Robotics» more  ICRA 2010»
15 years 4 months ago
Heteroscedastic Gaussian processes for data fusion in large scale terrain modeling
This paper presents a novel approach to data fusion for stochastic processes that model spatial data. It addresses the problem of data fusion in the context of large scale terrain ...
Shrihari Vasudevan, Fabio T. Ramos, Eric Nettleton...