no code implementations • 10 May 2024 • Ahmed Ali Abbasi, Namrata Vaswani
In this work, we develop and analyze a Gradient Descent (GD) based solution, called Alternating GD and Minimization (AltGDmin), for efficiently solving the low rank matrix completion (LRMC) in a federated setting.
no code implementations • 7 Nov 2023 • Silpa Babu, Namrata Vaswani
We aim to recover an $n \times q$ matrix, $\X^* =[ \x_1^*, \x_2^*, \cdots , \x_q^*]$ from $m$ independent linear projections of each of its $q$ columns, given by $\y_k :=\A_k\x_k^*$, $k \in [q]$.
no code implementations • 25 Sep 2023 • Ankit Pratap Singh, Namrata Vaswani
We also study the most natural solution for this problem, a geometric median based modification of the federated power method, and explain why it is not useful.
1 code implementation • 19 Dec 2022 • Silpa Babu, Sajan Goud Lingala, Namrata Vaswani
This claim is based on comparisons on 8 different retrospectively under sampled multi-coil dynamic MRI applications, sampled using either 1D Cartesian or 2D pseudo radial under sampling, at multiple sampling rates.
1 code implementation • 17 Aug 2022 • Konstantinos Konstantinidis, Namrata Vaswani, Aditya Ramamoorthy
For strong attacks, we demonstrate a reduction in the fraction of distorted gradients ranging from 16%-99% as compared to the prior state-of-the-art.
1 code implementation • 27 Jun 2022 • Silpa Babu, Sajan Goud Lingala, Namrata Vaswani
By general, we mean that our algorithm can be used for multiple accelerated dynamic MRI applications and multiple sampling rates (acceleration rates) and patterns with a single choice of parameters (no parameter tuning).
no code implementations • 20 Feb 2021 • Seyedehsara Nayer, Namrata Vaswani
Finally, it can also be efficiently federated with a communication cost of only $nr$ per node, instead of $nq$ for projected GD.
Information Theory Information Theory
no code implementations • 23 Jun 2020 • Namrata Vaswani
Phase retrieval (PR), also sometimes referred to as quadratic sensing, is a problem that occurs in numerous signal and image acquisition domains ranging from optics, X-ray crystallography, Fourier ptychography, sub-diffraction imaging, and astronomy.
1 code implementation • 14 Jun 2020 • Praneeth Narayanamurthy, Namrata Vaswani
An important setting where this is useful is for linearly data dependent noise that is sparse with support that changes enough over time.
1 code implementation • 28 Feb 2020 • Praneeth Narayanamurthy, Namrata Vaswani, Aditya Ramamoorthy
In this work we study the problem of Subspace Tracking with missing data (ST-miss) and outliers (Robust ST-miss).
1 code implementation • 18 Jul 2019 • Anindya B. Das, Aditya Ramamoorthy, Namrata Vaswani
Distributed matrix computations (matrix-vector and matrix-matrix multiplications) are at the heart of several tasks within the machine learning pipeline.
Information Theory Information Theory
1 code implementation • 13 Feb 2019 • Seyedehsara Nayer, Praneeth Narayanamurthy, Namrata Vaswani
We study the Low Rank Phase Retrieval (LRPR) problem defined as follows: recover an $n \times q$ matrix $X^*$ of rank $r$ from a different and independent set of $m$ phaseless (magnitude-only) linear projections of each of its columns.
1 code implementation • 6 Oct 2018 • Praneeth Narayanamurthy, Vahid Daneshpajooh, Namrata Vaswani
In this work, we show that a simple modification of our robust ST solution also provably solves ST-miss and robust ST-miss.
no code implementations • 11 Sep 2018 • Seyedehsara Nayer, Namrata Vaswani
This work takes the first steps towards solving the "phaseless subspace tracking" (PST) problem.
no code implementations • 1 Mar 2018 • Namrata Vaswani, Praneeth Narayanamurthy
Robust PCA (RPCA) refers to the problem of PCA when the data may be corrupted by outliers.
1 code implementation • ICML 2018 • Praneeth Narayanamurthy, Namrata Vaswani
We prove that NORST solves both the RST and the dynamic RPCA problems under weakened standard RPCA assumptions, two simple extra assumptions (slow subspace change and most outlier magnitudes lower bounded), and a few minor assumptions.
no code implementations • 26 Nov 2017 • Namrata Vaswani, Thierry Bouwmans, Sajid Javed, Praneeth Narayanamurthy
The problem of subspace learning or PCA in the presence of outliers is called robust subspace learning or robust PCA (RPCA).
no code implementations • 5 Oct 2017 • Han Guo, Namrata Vaswani
Video denoising refers to the problem of removing "noise" from a video sequence.
1 code implementation • 19 Sep 2017 • Namrata Vaswani, Praneeth Narayanamurthy
This work obtains novel finite sample guarantees for Principal Component Analysis (PCA).
2 code implementations • 24 May 2017 • Praneeth Narayanamurthy, Namrata Vaswani
Dynamic robust PCA refers to the dynamic (time-varying) extension of robust PCA (RPCA).
no code implementations • 10 Feb 2017 • Namrata Vaswani, Praneeth Narayanamurthy
We study Principal Component Analysis (PCA) in a setting where a part of the corrupting noise is data-dependent and, as a result, the noise and the true data are correlated.
no code implementations • NeurIPS 2016 • Namrata Vaswani, Han Guo
We obtain a correctness result for the standard eigenvalue decomposition (EVD) based solution to PCA under simple assumptions on the data-noise correlation.
no code implementations • NeurIPS 2016 • Namrata Vaswani, Han Guo
We obtain a correctness result for the standard eigenvalue decomposition (EVD) based solution to PCA under simple assumptions on the data-noise correlation.
no code implementations • 11 Mar 2015 • Brian Lois, Namrata Vaswani
In this work, we develop a practical modification of our recently proposed algorithm to solve both the online RPCA and online MC problems.