Search Results for author: Waheed U. Bajwa

Found 30 papers, 4 papers with code

Mitigating Data Injection Attacks on Federated Learning

no code implementations4 Dec 2023 Or Shalom, Amir Leshem, Waheed U. Bajwa

However, despite its advantages, federated learning can be susceptible to false data injection attacks.

Federated Learning

Structured Low-Rank Tensors for Generalized Linear Models

no code implementations5 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.

regression Tensor Decomposition

Accelerated gradient methods for nonconvex optimization: Escape trajectories from strict saddle points and convergence to local minima

no code implementations13 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.

Programming Wireless Security through Learning-Aided Spatiotemporal Digital Coding Metamaterial Antenna

no code implementations16 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.

Management

Domain-informed neural networks for interaction localization within astroparticle experiments

1 code implementation15 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.

FAST-PCA: A Fast and Exact Algorithm for Distributed Principal Component Analysis

no code implementations27 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.

Dimensionality Reduction

A Guide to Computational Reproducibility in Signal Processing and Machine Learning

no code implementations27 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.

BIG-bench Machine Learning

A hybrid model-based and learning-based approach for classification using limited number of training samples

no code implementations25 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.

Distributed Principal Subspace Analysis for Partitioned Big Data: Algorithms, Analysis, and Implementation

no code implementations11 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.

Dimensionality Reduction

Boundary Conditions for Linear Exit Time Gradient Trajectories Around Saddle Points: Analysis and Algorithm

no code implementations7 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.

A Linearly Convergent Algorithm for Distributed Principal Component Analysis

1 code implementation5 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.

Dimensionality Reduction

Exit Time Analysis for Approximations of Gradient Descent Trajectories Around Saddle Points

no code implementations1 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.

Scaling-up Distributed Processing of Data Streams for Machine Learning

no code implementations18 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.

BIG-bench Machine Learning Stochastic Optimization

Learning Product Graphs Underlying Smooth Graph Signals

no code implementations26 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.

Graph Learning

Distributed Stochastic Algorithms for High-rate Streaming Principal Component Analysis

no code implementations4 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.

Vocal Bursts Intensity Prediction

Tensor Regression Using Low-rank and Sparse Tucker Decompositions

no code implementations9 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 $m\ll n = \prod \nolimits_{i} n_i$.

regression

Adversary-resilient Distributed and Decentralized Statistical Inference and Machine Learning: An Overview of Recent Advances Under the Byzantine Threat Model

no code implementations23 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.

Decision Making

BRIDGE: Byzantine-resilient Decentralized Gradient Descent

2 code implementations21 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.

BIG-bench Machine Learning

Learning-Aided Physical Layer Attacks Against Multicarrier Communications in IoT

no code implementations1 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.

Disentanglement

Learning Mixtures of Separable Dictionaries for Tensor Data: Analysis and Algorithms

1 code implementation22 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.

Dictionary Learning

Identifiability of Kronecker-structured Dictionaries for Tensor Data

no code implementations10 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.

STARK: Structured Dictionary Learning Through Rank-one Tensor Recovery

no code implementations13 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.

Dictionary Learning

ByRDiE: Byzantine-resilient distributed coordinate descent for decentralized learning

no code implementations28 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.

ExSIS: Extended Sure Independence Screening for Ultrahigh-dimensional Linear Models

no code implementations21 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.

Stochastic Optimization from Distributed, Streaming Data in Rate-limited Networks

no code implementations25 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.

Stochastic Optimization

Human Action Attribute Learning From Video Data Using Low-Rank Representations

no code implementations23 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.

Action Recognition Attribute +3

Minimax Lower Bounds for Kronecker-Structured Dictionary Learning

no code implementations17 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.

Dictionary Learning

Cloud K-SVD: A Collaborative Dictionary Learning Algorithm for Big, Distributed Data

no code implementations25 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).

Dictionary Learning

Learning the nonlinear geometry of high-dimensional data: Models and algorithms

no code implementations21 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.

Clustering Vocal Bursts Intensity Prediction

Cannot find the paper you are looking for? You can Submit a new open access paper.