17 citations · 73 across the 11 of their papers we have counts for
12 papers
Learning From Crowdsourced Noisy Labels: A Signal Processing Perspective
Shahana Ibrahim, Panagiotis A. Traganitis, Xiao Fu +1
One of the primary catalysts fueling advances in artificial intelligence (AI) and machine learning (ML) is the availability of massive, curated datasets. A commonly used technique…
Deep Learning From Crowdsourced Labels: Coupled Cross-entropy Minimization, Identifiability, and Regularization
Shahana Ibrahim, Tri Nguyen, Xiao Fu
Using noisy crowdsourced labels from multiple annotators, a deep learning-based end-to-end (E2E) system aims to learn the label correction mechanism and the neural classifier simul…
Under-Counted Tensor Completion with Neural Incorporation of Attributes
Shahana Ibrahim, Xiao Fu, Rebecca Hutchinson +1
Systematic under-counting effects are observed in data collected across many disciplines, e.g., epidemiology and ecology. Under-counted tensor completion (UC-TC) is well-motivated…
Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach
Tri Nguyen, Shahana Ibrahim, Xiao Fu
The recent integration of deep learning and pairwise similarity annotation-based constrained clustering -- i.e., (DCC) -- has proven effectiv…
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…
Stochastic Mirror Descent for Low-Rank Tensor Decomposition Under Non-Euclidean Losses
Wenqiang Pu, Shahana Ibrahim, Xiao Fu +1
This work considers low-rank canonical polyadic decomposition (CPD) under a class of non-Euclidean loss functions that frequently arise in statistical machine learning and signal p…
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…
Recovering Joint Probability of Discrete Random Variables from Pairwise Marginals
Shahana Ibrahim, Xiao Fu
Learning the joint probability of random variables (RVs) is the cornerstone of statistical signal processing and machine learning. However, direct nonparametric estimation for high…
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…
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…
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…
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…