activity
20172021
most citedCrowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms

17 citations · 17 across the 2 of their papers we have counts for

collaborators

6 papers

cs.LG2021

Crowdsourcing via Annotator Co-occurrence Imputation and Provable Symmetric Nonnegative Matrix Factorization

Shahana Ibrahim, Xiao Fu

Unsupervised learning of the Dawid-Skene (D&S) model from noisy, incomplete and crowdsourced annotations has been a long-standing challenge, and is a critical step towards reliably…

cs.LG2020

Mixed Membership Graph Clustering via Systematic Edge Query

Shahana Ibrahim, Xiao Fu

This work considers clustering nodes of a largely incomplete graph. Under the problem setting, only a small amount of queries about the edges can be made, but the entire graph is n…

eess.SP2020

On Recoverability of Randomly Compressed Tensors with Low CP Rank

Shahana Ibrahim, Xiao Fu, Xingguo Li

Our interest lies in the recoverability properties of compressed tensors under the \textit{canonical polyadic decomposition} (CPD) model. The considered problem is well-motivated i…

cs.LG201917 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…

eess.SP2019

Block-Randomized Stochastic Proximal Gradient for Low-Rank Tensor Factorization

Xiao Fu, Shahana Ibrahim, Hoi-To Wai +2

This work considers the problem of computing the canonical polyadic decomposition (CPD) of large tensors. Prior works mostly leverage data sparsity to handle this problem, which is…

stat.AP2017

Estimating Phase Duration for SPaT Messages

Shahana Ibrahim, Dileep Kalathil, Rene O. Sanchez +1

A SPaT (Signal Phase and Timing) message describes for each lane the current phase at a signalized intersection together with an estimate of the residual time of that phase. Accura…