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IJCAI
2001
15 years 7 months ago
Approximate inference for first-order probabilistic languages
A new, general approach is described for approximate inference in first-order probabilistic languages, using Markov chain Monte Carlo (MCMC) techniques in the space of concrete po...
Hanna Pasula, Stuart J. Russell
NIPS
2001
15 years 7 months ago
Linking Motor Learning to Function Approximation: Learning in an Unlearnable Force Field
Reaching movements require the brain to generate motor commands that rely on an internal model of the task's dynamics. Here we consider the errors that subjects make early in...
O. Donchin, Reza Shadmehr
NIPS
2003
15 years 7 months ago
Approximate Policy Iteration with a Policy Language Bias
We study an approach to policy selection for large relational Markov Decision Processes (MDPs). We consider a variant of approximate policy iteration (API) that replaces the usual...
Alan Fern, Sung Wook Yoon, Robert Givan
ICMCS
2010
IEEE
146views Multimedia» more  ICMCS 2010»
15 years 7 months ago
Privacy-preserving approximation of L1 distance for multimedia applications
Alice and Bob possess sequences x and y respectively and would like to compute the 1 distance, namely x - y 1 under privacy and communication constraints. The privacy constraint r...
Shantanu Rane, Wei Sun, Anthony Vetro
CORR
2008
Springer
96views Education» more  CORR 2008»
15 years 6 months ago
Approximating the Gaussian Multiple Description Rate Region Under Symmetric Distortion Constraints
We consider multiple description (MD) coding for the Gaussian source with K descriptions under the symmetric meansquared error (MSE) distortion constraints, and provide an approxim...
Chao Tian, Soheil Mohajer, Suhas N. Diggavi