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» Approximate algorithms for neural-Bayesian approaches
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ICML
2009
IEEE
15 years 4 months ago
Split variational inference
We propose a deterministic method to evaluate the integral of a positive function based on soft-binning functions that smoothly cut the integral into smaller integrals that are ea...
Guillaume Bouchard, Onno Zoeter
64
Voted
SODA
2008
ACM
122views Algorithms» more  SODA 2008»
14 years 11 months ago
Fast approximation of the permanent for very dense problems
Approximation of the permanent of a matrix with nonnegative entries is a well studied problem. The most successful approach to date for general matrices uses Markov chains to appr...
Mark Huber, Jenny Law
COMPGEOM
1993
ACM
15 years 1 months ago
Approximating Center Points with Iterated Radon Points
We give a practical and provably good Monte Carlo algorithm for approximating center points. Let P be a set of n points in IRd . A point c ∈ IRd is a β-center point of P if eve...
Kenneth L. Clarkson, David Eppstein, Gary L. Mille...
AAAI
2010
14 years 11 months ago
Reinforcement Learning via AIXI Approximation
This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. This approach is based on a direct approximation of AIXI, a Bayesian...
Joel Veness, Kee Siong Ng, Marcus Hutter, David Si...
71
Voted
ECAI
2008
Springer
14 years 11 months ago
Exploiting locality of interactions using a policy-gradient approach in multiagent learning
In this paper, we propose a policy gradient reinforcement learning algorithm to address transition-independent Dec-POMDPs. This approach aims at implicitly exploiting the locality...
Francisco S. Melo