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» From Algorithmic to Subjective Randomness
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ITS
2010
Springer
178views Multimedia» more  ITS 2010»
15 years 2 months ago
Learning What Works in ITS from Non-traditional Randomized Controlled Trial Data
The traditional, well established approach to finding out what works in education research is to run a randomized controlled trial (RCT) using a standard pretest and posttest desig...
Zachary A. Pardos, Matthew D. Dailey, Neil T. Heff...
ICCV
2011
IEEE
13 years 9 months ago
Perturb-and-MAP Random Fields: Using Discrete Optimization\\to Learn and Sample from Energy Models
We propose a novel way to induce a random field from an energy function on discrete labels. It amounts to locally injecting noise to the energy potentials, followed by finding t...
George Papandreou, Alan L. Yuille
ISAAC
1998
Springer
66views Algorithms» more  ISAAC 1998»
15 years 1 months ago
A Parallel Algorithm for Sampling Matchings from an Almost Uniform Distribution
In this paper we present a randomized parallel algorithm to sample matchings from an almost uniform distribution on the set of matchings of all sizes in a graph. First we prove th...
Josep Díaz, Jordi Petit, Panagiotis Psychar...
FOCS
2000
IEEE
15 years 2 months ago
Extracting Randomness from Samplable Distributions
Randomness extractors convert weak sources of randomness into an almost uniform distribution; the conversion uses a small amount of pure randomness. In algorithmic applications, t...
Luca Trevisan, Salil P. Vadhan
COCO
2006
Springer
100views Algorithms» more  COCO 2006»
15 years 1 months 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