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AIPS
2010
13 years 9 months ago
When Policies Can Be Trusted: Analyzing a Criteria to Identify Optimal Policies in MDPs with Unknown Model Parameters
Computing a good policy in stochastic uncertain environments with unknown dynamics and reward model parameters is a challenging task. In a number of domains, ranging from space ro...
Emma Brunskill
SIAMIS
2011
13 years 2 months ago
Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models
Abstract. Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or superresolution, can be addressed by maxi...
Matthias W. Seeger, Hannes Nickisch
CORR
2010
Springer
135views Education» more  CORR 2010»
13 years 7 months ago
A stochastic analysis of greedy routing in a spatially-dependent sensor network
For a sensor network, as tractable spatially-dependent node deployment model is presented with the property that the density is inversely proportional to the sink distance. A stoc...
H. Paul Keeler
NIPS
2004
13 years 8 months ago
Log-concavity Results on Gaussian Process Methods for Supervised and Unsupervised Learning
Log-concavity is an important property in the context of optimization, Laplace approximation, and sampling; Bayesian methods based on Gaussian process priors have become quite pop...
Liam Paninski
MOBIHOC
2010
ACM
13 years 5 months ago
Near optimal multi-application allocation in shared sensor networks
Recent years have witnessed the emergence of shared sensor networks as integrated infrastructure for multiple applications. It is important to allocate multiple applications in a ...
You Xu, Abusayeed Saifullah, Yixin Chen, Chenyang ...