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20172022
most citedCrowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms

17 citations · 37 across the 5 of their papers we have counts for

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6 papers · 1 filter

cs.LG2021

Multi-version Tensor Completion for Time-delayed Spatio-temporal Data

Cheng Qian, Nikos Kargas, Cao Xiao +3

Real-world spatio-temporal data is often incomplete or inaccurate due to various data loading delays. For example, a location-disease-time tensor of case counts can have multiple d…

cs.LG2020★ 3 cited

STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization

Nikos Kargas, Cheng Qian, Nicholas D. Sidiropoulos +3

Accurate prediction of the transmission of epidemic diseases such as COVID-19 is crucial for implementing effective mitigation measures. In this work, we develop a tensor method to…

cs.LG2020

Information-theoretic Feature Selection via Tensor Decomposition and Submodularity

Magda Amiridi, Nikos Kargas, Nicholas D. Sidiropoulos

Feature selection by maximizing high-order mutual information between the selected feature vector and a target variable is the gold standard in terms of selecting the best subset o…

cs.LG2019★ 17 cited

Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms

Shahana Ibrahim, Xiao Fu, Nikos Kargas +1

The data deluge comes with high demands for data labeling. Crowdsourcing (or, more generally, ensemble learning) techniques aim to produce accurate labels via integrating noisy, no…

cs.LG2019

Nonlinear System Identification via Tensor Completion

Nikos Kargas, Nicholas D. Sidiropoulos

Function approximation from input and output data pairs constitutes a fundamental problem in supervised learning. Deep neural networks are currently the most popular method for lea…

cs.LG2017★ 2 cited

Completing a joint PMF from projections: a low-rank coupled tensor factorization approach

Nikos Kargas, Nicholas D. Sidiropoulos

There has recently been considerable interest in completing a low-rank matrix or tensor given only a small fraction (or few linear combinations) of its entries. Related approaches…