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no code implementations • 24 Jan 2023 • Kenneth L. Clarkson, Shashanka Ubaru, Elizabeth Yang

The ensemble of a particular vector space and a prescribed set of vector operations (including one addition-like for "bundling" and one outer-product-like for "binding") form a *vector symbolic architecture* (VSA).

no code implementations • 19 Sep 2022 • Ismail Yunus Akhalwaya, Shashanka Ubaru, Kenneth L. Clarkson, Mark S. Squillante, Vishnu Jejjala, Yang-Hui He, Kugendran Naidoo, Vasileios Kalantzis, Lior Horesh

TDA is a powerful technique for extracting complex and valuable shape-related summaries of high-dimensional data.

no code implementations • 10 Feb 2022 • Paz Fink Shustin, Shashanka Ubaru, Vasileios Kalantzis, Lior Horesh, Haim Avron

In this paper, we present a novel surrogate model for representation learning and uncertainty quantification, which aims to deal with data of moderate to high dimensions.

no code implementations • 16 Sep 2021 • Venkatesan T. Chakaravarthy, Shivmaran S. Pandian, Saurabh Raje, Yogish Sabharwal, Toyotaro Suzumura, Shashanka Ubaru

We present distributed algorithms for training dynamic Graph Neural Networks (GNN) on large scale graphs spanning multi-node, multi-GPU systems.

no code implementations • 5 Aug 2021 • Shashanka Ubaru, Ismail Yunus Akhalwaya, Mark S. Squillante, Kenneth L. Clarkson, Lior Horesh

In this paper, we completely overhaul the QTDA algorithm to achieve an improved exponential speedup and depth complexity of $O(n\log(1/(\delta\epsilon)))$.

no code implementations • 13 Oct 2020 • Vassilis Kalantzis, Georgios Kollias, Shashanka Ubaru, Athanasios N. Nikolakopoulos, Lior Horesh, Kenneth L. Clarkson

This paper considers the problem of updating the rank-k truncated Singular Value Decomposition (SVD) of matrices subject to the addition of new rows and/or columns over time.

no code implementations • 10 Sep 2020 • Shashanka Ubaru, Lior Horesh, Guy Cohen

Thus, estimation of state uncertainty is paramount for both eminent risk assessment, as well as for closing the tracing-testing loop by optimal testing prescription.

1 code implementation • NeurIPS 2020 • Shashanka Ubaru, Sanjeeb Dash, Arya Mazumdar, Oktay Gunluk

We then present a hierarchical partitioning approach that exploits the label hierarchy in large scale problems to divide up the large label space and create smaller sub-problems, which can then be solved independently via the grouping approach.

1 code implementation • ICLR 2020 • Osman Asif Malik, Shashanka Ubaru, Lior Horesh, Misha E. Kilmer, Haim Avron

In recent years, a variety of graph neural networks (GNNs) have been successfully applied for representation learning and prediction on such graphs.

no code implementations • 25 Sep 2019 • Shashanka Ubaru, Jie Chen

These approaches are supervised; a predictive task with ground-truth labels is used to drive the learning.

no code implementations • 8 Oct 2018 • Shashanka Ubaru, Abd-Krim Seghouane, Yousef Saad

In this paper, we consider the problem of simultaneously estimating the dimension of the principal (dominant) subspace of these covariance matrices and obtaining an approximation to the subspace.

no code implementations • 1 Jun 2018 • Tayo Ajayi, David Mildebrath, Anastasios Kyrillidis, Shashanka Ubaru, Georgios Kollias, Kristofer Bouchard

We present theoretical results on the convergence of \emph{non-convex} accelerated gradient descent in matrix factorization models with $\ell_2$-norm loss.

no code implementations • 1 Nov 2017 • Shashanka Ubaru, Yousef Saad

To tackle these problems, we consider algorithms that are based primarily on coarsening techniques, possibly combined with random sampling.

no code implementations • ICML 2017 • Shashanka Ubaru, Arya Mazumdar

In this work, we propose a novel approach based on group testing to solve such large multilabel classification problems with sparse label vectors.

no code implementations • NeurIPS 2017 • Kristofer E. Bouchard, Alejandro F. Bujan, Farbod Roosta-Khorasani, Shashanka Ubaru, Prabhat, Antoine M. Snijders, Jian-Hua Mao, Edward F. Chang, Michael W. Mahoney, Sharmodeep Bhattacharyya

The increasing size and complexity of scientific data could dramatically enhance discovery and prediction for basic scientific applications.

no code implementations • 13 Apr 2017 • Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff

We thus effectively compute a histogram of the spectrum, which can stand in for the true singular values in many applications.

no code implementations • 19 Aug 2016 • Shashanka Ubaru, Yousef Saad, Abd-Krim Seghouane

In this paper, we present two computationally inexpensive techniques to estimate the approximate ranks of such large matrices.

no code implementations • 30 Dec 2015 • Shashanka Ubaru, Arya Mazumdar, Yousef Saad

In this paper, we show how matrices from error correcting codes can be used to find such low rank approximations and matrix decompositions, and extend the framework to linear least squares regression problems.

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