no code implementations • 4 Dec 2023 • Or Shalom, Amir Leshem, Waheed U. Bajwa
However, despite its advantages, federated learning can be susceptible to false data injection attacks.
no code implementations • 5 Aug 2023 • Batoul Taki, Anand D. Sarwate, Waheed U. Bajwa
This result can also be specialised to lower bound the estimation error in CP and Tucker-structured GLMs.
no code implementations • 13 Jul 2023 • Rishabh Dixit, Mert Gurbuzbalaban, Waheed U. Bajwa
This work also develops two metrics of asymptotic rate of convergence and divergence, and evaluates these two metrics for several popular standard accelerated methods such as the NAG, and Nesterov's accelerated gradient with constant momentum (NCM) near strict saddle points.
no code implementations • 16 Nov 2022 • Alireza Nooraiepour, Shaghayegh Vosoughitabar, Chung-Tse Michael Wu, Waheed U. Bajwa, Narayan B. Mandayam
Physical layer (PHY) security has been put forth as a cost-effective alternative to cryptographic mechanisms that can circumvent the need for explicit key exchange between communication devices, owing to the fact that PHY security relies on the physics of the signal transmission for providing security.
1 code implementation • 15 Dec 2021 • Shixiao Liang, Aaron Higuera, Christina Peters, Venkat Roy, Waheed U. Bajwa, Hagit Shatkay, Christopher D. Tunnell
The resulting Domain-informed Neural Network (DiNN) limits the receptive fields of the neurons in the initial feature encoding layers in order to account for the spatially localized nature of the signals produced within the TPC.
no code implementations • 27 Aug 2021 • Arpita Gang, Waheed U. Bajwa
While PCA is often thought of as a dimensionality reduction method, the purpose of PCA is actually two-fold: dimension reduction and uncorrelated feature learning.
no code implementations • 27 Aug 2021 • Joseph Shenouda, Waheed U. Bajwa
Computational reproducibility is a growing problem that has been extensively studied among computational researchers and within the signal processing and machine learning research community.
no code implementations • 25 Jun 2021 • Alireza Nooraiepour, Waheed U. Bajwa, Narayan B. Mandayam
In this paper, a hybrid classification method -- termed HyPhyLearn -- is proposed that exploits both the physics-based statistical models and the learning-based classifiers.
no code implementations • 31 May 2021 • Batoul Taki, Mohsen Ghassemi, Anand D. Sarwate, Waheed U. Bajwa
This paper considers the problem of matrix-variate logistic regression.
no code implementations • 11 Mar 2021 • Arpita Gang, Bingqing Xiang, Waheed U. Bajwa
This has led to the study of distributed PSA/PCA solutions, in which the data are partitioned across multiple machines and an estimate of the principal subspace is obtained through collaboration among the machines.
no code implementations • 7 Jan 2021 • Rishabh Dixit, Mert Gurbuzbalaban, Waheed U. Bajwa
This paper concerns convergence of first-order discrete methods to a local minimum of nonconvex optimization problems that comprise strict-saddle points within the geometrical landscape.
1 code implementation • 5 Jan 2021 • Arpita Gang, Waheed U. Bajwa
This paper focuses on the dual objective of PCA, namely, dimensionality reduction and decorrelation of features, but in a distributed setting.
no code implementations • 1 Jun 2020 • Rishabh Dixit, Mert Gurbuzbalaban, Waheed U. Bajwa
This paper considers the problem of understanding the exit time for trajectories of gradient-related first-order methods from saddle neighborhoods under some initial boundary conditions.
no code implementations • 18 May 2020 • Matthew Nokleby, Haroon Raja, Waheed U. Bajwa
This paper reviews recently developed methods that focus on large-scale distributed stochastic optimization in the compute- and bandwidth-limited regime, with an emphasis on convergence analysis that explicitly accounts for the mismatch between computation, communication and streaming rates.
no code implementations • 26 Feb 2020 • Muhammad Asad Lodhi, Waheed U. Bajwa
However, in cases where the underlying graph is unavailable, it needs to be learned from the data itself for data representation, data processing and inference purposes.
no code implementations • 4 Jan 2020 • Haroon Raja, Waheed U. Bajwa
The analysis of DM-Krasulina shows that it can also achieve order-optimal estimation error rates under appropriate conditions, even when some samples have to be discarded within the network due to communication latency.
no code implementations • 9 Nov 2019 • Talal Ahmed, Haroon Raja, Waheed U. Bajwa
It focuses on the task of estimating the regression tensor from $m$ realizations of the response variable and the predictors where $m\ll n = \prod \nolimits_{i} n_i$.
no code implementations • 23 Aug 2019 • Zhixiong Yang, Arpita Gang, Waheed U. Bajwa
While the last few decades have witnessed a huge body of work devoted to inference and learning in distributed and decentralized setups, much of this work assumes a non-adversarial setting in which individual nodes---apart from occasional statistical failures---operate as intended within the algorithmic framework.
2 code implementations • 21 Aug 2019 • Cheng Fang, Zhixiong Yang, Waheed U. Bajwa
The focus of this paper is on robustification of decentralized learning in the presence of nodes that have undergone Byzantine failures.
no code implementations • 1 Aug 2019 • Alireza Nooraiepour, Waheed U. Bajwa, Narayan B. Mandayam
It is in this vein that PHY spoofing performance of adversaries equipped with supervised and unsupervised ML tools are investigated in this paper.
1 code implementation • 22 Mar 2019 • Mohsen Ghassemi, Zahra Shakeri, Anand D. Sarwate, Waheed U. Bajwa
This work addresses the problem of learning sparse representations of tensor data using structured dictionary learning.
no code implementations • 10 Dec 2017 • Zahra Shakeri, Anand D. Sarwate, Waheed U. Bajwa
This paper derives sufficient conditions for local recovery of coordinate dictionaries comprising a Kronecker-structured dictionary that is used for representing $K$th-order tensor data.
no code implementations • 13 Nov 2017 • Mohsen Ghassemi, Zahra Shakeri, Anand D. Sarwate, Waheed U. Bajwa
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 structure on the dictionary underlying the data.
no code implementations • 28 Aug 2017 • Zhixiong Yang, Waheed U. Bajwa
Distributed machine learning algorithms enable learning of models from datasets that are distributed over a network without gathering the data at a centralized location.
no code implementations • 21 Aug 2017 • Talal Ahmed, Waheed U. Bajwa
Correlation-based variable screening, in which one leverages marginal correlations for removal of irrelevant variables from the model prior to statistical inference, can be used to overcome this challenge.
no code implementations • 25 Apr 2017 • Matthew Nokleby, Waheed U. Bajwa
Motivated by machine learning applications in networks of sensors, internet-of-things (IoT) devices, and autonomous agents, we propose techniques for distributed stochastic convex learning from high-rate data streams.
no code implementations • 23 Dec 2016 • Tong Wu, Prudhvi Gurram, Raghuveer M. Rao, Waheed U. Bajwa
Representation of human actions as a sequence of human body movements or action attributes enables the development of models for human activity recognition and summarization.
no code implementations • 17 May 2016 • Zahra Shakeri, Waheed U. Bajwa, Anand D. Sarwate
This paper finds fundamental limits on the sample complexity of estimating dictionaries for tensor data by proving a lower bound on the minimax risk.
no code implementations • 25 Dec 2014 • Haroon Raja, Waheed U. Bajwa
In contrast to previous works on subspace-based data representations, this paper focuses on the geometric structure of a union of subspaces (UoS).
no code implementations • 21 Dec 2014 • Tong Wu, Waheed U. Bajwa
This paper revisits the problem of data-driven learning of these geometric structures and puts forth two new nonlinear geometric models for data describing "related" objects/phenomena.