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AAAI
2006
13 years 10 months ago
Compact, Convex Upper Bound Iteration for Approximate POMDP Planning
Partially observable Markov decision processes (POMDPs) are an intuitive and general way to model sequential decision making problems under uncertainty. Unfortunately, even approx...
Tao Wang, Pascal Poupart, Michael H. Bowling, Dale...
TIT
2002
65views more  TIT 2002»
13 years 8 months ago
On the importance of combining wavelet-based nonlinear approximation with coding strategies
This paper provides a mathematical analysis of transform compression in its relationship to linear and nonlinear approximation theory. Contrasting linear and nonlinear approximatio...
Albert Cohen, Ingrid Daubechies, Onur G. Guleryuz,...
PODC
2005
ACM
14 years 2 months ago
Facility location: distributed approximation
In this paper, we initiate the study of the approximability of the facility location problem in a distributed setting. In particular, we explore a trade-off between the amount of...
Thomas Moscibroda, Roger Wattenhofer
CP
2006
Springer
14 years 23 days ago
Approximability of Integer Programming with Generalised Constraints
We study a family of problems, called Maximum Solution, where the objective is to maximise a linear goal function over the feasible integer assignments to a set of variables subjec...
Peter Jonsson, Fredrik Kuivinen, Gustav Nordh
ICML
2010
IEEE
13 years 10 months ago
Learning Fast Approximations of Sparse Coding
In Sparse Coding (SC), input vectors are reconstructed using a sparse linear combination of basis vectors. SC has become a popular method for extracting features from data. For a ...
Karol Gregor, Yann LeCun