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ECCC
2011
183views ECommerce» more  ECCC 2011»
13 years 3 months ago
Extractors and Lower Bounds for Locally Samplable Sources
We consider the problem of extracting randomness from sources that are efficiently samplable, in the sense that each output bit of the sampler only depends on some small number d ...
Anindya De, Thomas Watson
NIPS
2007
13 years 10 months ago
Bayesian Inference for Spiking Neuron Models with a Sparsity Prior
Generalized linear models are the most commonly used tools to describe the stimulus selectivity of sensory neurons. Here we present a Bayesian treatment of such models. Using the ...
Sebastian Gerwinn, Jakob Macke, Matthias Seeger, M...
COCO
2006
Springer
100views Algorithms» more  COCO 2006»
14 years 6 days ago
How to Get More Mileage from Randomness Extractors
Let C be a class of distributions over {0, 1}n . A deterministic randomness extractor for C is a function E : {0, 1}n {0, 1}m such that for any X in C the distribution E(X) is sta...
Ronen Shaltiel
TSP
2010
13 years 3 months ago
Universal randomized switching
Abstract--In this paper, we consider a competitive approach to sequential decision problems, suitable for a variety of signal processing applications where at each of a succession ...
Suleyman Serdar Kozat, Andrew C. Singer
DMIN
2006
125views Data Mining» more  DMIN 2006»
13 years 10 months ago
Privacy-Preserving Bayesian Network Learning From Heterogeneous Distributed Data
In this paper, we propose a post randomization technique to learn a Bayesian network (BN) from distributed heterogeneous data, in a privacy sensitive fashion. In this case, two or ...
Jianjie Ma, Krishnamoorthy Sivakumar