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» From Algorithmic to Subjective Randomness
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179
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SIGIR
2011
ACM
14 years 7 months ago
Learning to rank from a noisy crowd
We study how to best use crowdsourced relevance judgments learning to rank [1, 7]. We integrate two lines of prior work: unreliable crowd-based binary annotation for binary classi...
Abhimanu Kumar, Matthew Lease
169
Voted
KDD
2012
ACM
244views Data Mining» more  KDD 2012»
13 years 7 months ago
Open domain event extraction from twitter
Tweets are the most up-to-date and inclusive stream of information and commentary on current events, but they are also fragmented and noisy, motivating the need for systems that c...
Alan Ritter, Mausam, Oren Etzioni, Sam Clark
173
Voted
KDD
2012
ACM
178views Data Mining» more  KDD 2012»
13 years 7 months ago
Mining event periodicity from incomplete observations
Advanced technology in GPS and sensors enables us to track physical events, such as human movements and facility usage. Periodicity analysis from the recorded data is an important...
Zhenhui Li, Jingjing Wang, Jiawei Han
184
Voted
SIGIR
2012
ACM
13 years 7 months ago
Inferring missing relevance judgments from crowd workers via probabilistic matrix factorization
In crowdsourced relevance judging, each crowd worker typically judges only a small number of examples, yielding a sparse and imbalanced set of judgments in which relatively few wo...
Hyun Joon Jung, Matthew Lease
ICASSP
2010
IEEE
15 years 4 months ago
Latent-variable decomposition based dereverberation of monaural and multi-channel signals
We present an algorithm to dereverberate single- and multi-channel audio recordings. The proposed algorithm models the magnitude spectrograms of clean audio signals as histograms ...
Rita Singh, Bhiksha Raj, Paris Smaragdis