activity
20062025
most citedGeographic Gossip: Efficient Averaging for Sensor Networks

194 citations · 270 across the 13 of their papers we have counts for

collaborators
Showing stat.MLShow all

6 papers · 1 filter

stat.ML20232 cited

Structured Low-Rank Tensors for Generalized Linear Models

Batoul Taki, Anand D. Sarwate, Waheed U. Bajwa

Recent works have shown that imposing tensor structures on the coefficient tensor in regression problems can lead to more reliable parameter estimation and lower sample complexity…

stat.ML2019

Improved Differentially Private Decentralized Source Separation for fMRI Data

Hafiz Imtiaz, Jafar Mohammadi, Rogers Silva +4

Blind source separation algorithms such as independent component analysis (ICA) are widely used in the analysis of neuroimaging data. In order to leverage larger sample sizes, diff…

stat.ML2019

Optimal Rates for Learning Hidden Tree Structures

Konstantinos E. Nikolakakis, Dionysios S. Kalogerias, Anand D. Sarwate

We provide high probability finite sample complexity guarantees for hidden non-parametric structure learning of tree-shaped graphical models, whose hidden and observable nodes are…

stat.ML2018

Predictive Learning on Hidden Tree-Structured Ising Models

Konstantinos E. Nikolakakis, Dionysios S. Kalogerias, Anand D. Sarwate

We provide high-probability sample complexity guarantees for exact structure recovery and accurate predictive learning using noise-corrupted samples from an acyclic (tree-shaped) g…

stat.ML2018

Distributed Differentially-Private Algorithms for Matrix and Tensor Factorization

Hafiz Imtiaz, Anand D. Sarwate

In many signal processing and machine learning applications, datasets containing private information are held at different locations, requiring the development of distributed priva…

stat.ML2017

STARK: Structured Dictionary Learning Through Rank-one Tensor Recovery

Mohsen Ghassemi, Zahra Shakeri, Anand D. Sarwate +1

In recent years, a class of dictionaries have been proposed for multidimensional (tensor) data representation that exploit the structure of tensor data by imposing a Kronecker stru…