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ICCV
2011
IEEE
12 years 7 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
SODA
2000
ACM
85views Algorithms» more  SODA 2000»
13 years 9 months ago
Improved bounds on the sample complexity of learning
We present a new general upper bound on the number of examples required to estimate all of the expectations of a set of random variables uniformly well. The quality of the estimat...
Yi Li, Philip M. Long, Aravind Srinivasan
DKE
2006
67views more  DKE 2006»
13 years 7 months ago
Indexed-based density biased sampling for clustering applications
Density biased sampling (DBS) has been proposed to address the limitations of Uniform sampling, by producing the desired probability distribution in the sample. The ease of produc...
Alexandros Nanopoulos, Yannis Theodoridis, Yannis ...
SIGIR
2008
ACM
13 years 7 months ago
A simple and efficient sampling method for estimating AP and NDCG
We consider the problem of large scale retrieval evaluation. Recently two methods based on random sampling were proposed as a solution to the extensive effort required to judge te...
Emine Yilmaz, Evangelos Kanoulas, Javed A. Aslam
MIDDLEWARE
2004
Springer
14 years 1 months ago
The Peer Sampling Service: Experimental Evaluation of Unstructured Gossip-Based Implementations
In recent years, the gossip-based communication model in large-scale distributed systems has become a general paradigm with important applications which include information dissemi...
Márk Jelasity, Rachid Guerraoui, Anne-Marie...