Search Results for author:
Found 11 papers, 4 papers with code
Learning From Crowdsourced Noisy Labels: A Signal Processing Perspective
One of the primary catalysts fueling advances in artificial intelligence (AI) and machine learning (ML) is the availability of massive, curated datasets.
Deep Learning From Crowdsourced Labels: Coupled Cross-entropy Minimization, Identifiability, and Regularization
The contribution of this work is twofold: First, performance guarantees of the CCEM criterion are presented.
Under-Counted Tensor Completion with Neural Incorporation of Attributes
Systematic under-counting effects are observed in data collected across many disciplines, e. g., epidemiology and ecology.
Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach
The recent integration of deep learning and pairwise similarity annotation-based constrained clustering -- i. e., $\textit{deep constrained clustering}$ (DCC) -- has proven effective for incorporating weak supervision into massive data clustering: Less than 1% of pair similarity annotations can often substantially enhance the clustering accuracy.
Crowdsourcing via Annotator Co-occurrence Imputation and Provable Symmetric Nonnegative Matrix Factorization
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 labeling massive data.
Stochastic Mirror Descent for Low-Rank Tensor Decomposition Under Non-Euclidean Losses
This work offers a unified stochastic algorithmic framework for large-scale CPD decomposition under a variety of non-Euclidean loss functions.
Mixed Membership Graph Clustering via Systematic Edge Query
This work aims at learning mixed membership of nodes using queried edges.
Recovering Joint Probability of Discrete Random Variables from Pairwise Marginals
Recent work has proposed to recover the joint probability mass function (PMF) of an arbitrary number of RVs from three-dimensional marginals, leveraging the algebraic properties of low-rank tensor decomposition and the (unknown) dependence among the RVs.
On Recoverability of Randomly Compressed Tensors with Low CP Rank
Our interest lies in the recoverability properties of compressed tensors under the \textit{canonical polyadic decomposition} (CPD) model.
Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms
The data deluge comes with high demands for data labeling.
Block-Randomized Stochastic Proximal Gradient for Low-Rank Tensor Factorization
In this work, we propose a stochastic optimization framework for large-scale CPD with constraints/regularizations.
