Search Results for author:
Found 36 papers, 7 papers with code
Sensitivity Curve Maximization: Attacking Robust Aggregators in Distributed Learning
In distributed learning agents aim at collaboratively solving a global learning problem.
Fronthaul-Constrained Distributed Radar Sensing
In this paper, we study a network of distributed radar sensors that collaboratively perform sensing tasks by transmitting their quantized radar signals over capacity-constrained fronthaul links to a central unit for joint processing.
A Graph Signal Processing Perspective of Network Multiple Hypothesis Testing with False Discovery Rate Control
We consider a multiple hypothesis testing problem in a sensor network over the joint spatio-temporal domain.
Fast and Robust Sparsity-Aware Block Diagonal Representation
The block diagonal structure of an affinity matrix is a commonly desired property in cluster analysis because it represents clusters of feature vectors by non-zero coefficients that are concentrated in blocks.
Attentional Graph Neural Networks for Robust Massive Network Localization
To tap the potential of GNNs in regression, this paper integrates GNNs with attention mechanism, a technique that revolutionized sequential learning tasks with its adaptability and robustness, to tackle a challenging nonlinear regression problem: network localization.
A Data-driven Deep Learning Approach for Bitcoin Price Forecasting
Bitcoin as a cryptocurrency has been one of the most important digital coins and the first decentralized digital currency.
Low-Rank Tensor Completion via Novel Sparsity-Inducing Regularizers
To alleviate the bias generated by the l1-norm in the low-rank tensor completion problem, nonconvex surrogates/regularizers have been suggested to replace the tensor nuclear norm, although both can achieve sparsity.
Robust Low-Rank Matrix Completion via a New Sparsity-Inducing Regularizer
Moreover, the closed-form solution to its Moreau envelope, namely, the proximity operator, is derived.
Emergency Response Person Localization and Vital Sign Estimation Using a Semi-Autonomous Robot Mounted SFCW Radar
The large number and scale of natural and man-made disasters have led to an urgent demand for technologies that enhance the safety and efficiency of search and rescue teams.
Attacks on Robust Distributed Learning Schemes via Sensitivity Curve Maximization
Classically, aggregation in distributed learning is based on averaging, which is statistically efficient, but susceptible to attacks by even a small number of malicious agents.
Sparse Array Design for Dual-Function Radar-Communications System
The problem of sparse array design for dual-function radar-communications is investigated.
Sparse Array Beamformer Design via ADMM
Moreover, the proposed ADMM algorithm outperforms the SDR, SDR-V, and SCA methods, in terms of computational complexity.
Convergence Analysis of Consensus-ADMM for General QCQP
We analyze the convergence properties of the consensus-alternating direction method of multipliers (ADMM) for solving general quadratically constrained quadratic programs.
Robust and Efficient Aggregation for Distributed Learning
This is followed by an aggregation step, which traditionally takes the form of a (weighted) average.
Low-Rank and Row-Sparse Decomposition for Joint DOA Estimation and Distorted Sensor Detection
Distorted sensors could occur randomly and may lead to the breakdown of a sensor array system.
Off-Grid Direction-of-Arrival Estimation Using Second-Order Taylor Approximation
The problem of off-grid direction-of-arrival (DOA) estimation is investigated.
Minimax Robust Landmine Detection Using Forward-Looking Ground-Penetrating Radar
We propose a robust likelihood-ratio test (LRT) to detect landmines and unexploded ordnance using a forward-looking ground-penetrating radar.
Multiple Hypothesis Testing Framework for Spatial Signals
The benefits of our method are illustrated by applications to spatially propagating radio waves.
Robust Regularized Locality Preserving Indexing for Fiedler Vector Estimation
The Fiedler vector of a connected graph is the eigenvector associated with the algebraic connectivity of the graph Laplacian and it provides substantial information to learn the latent structure of a graph.
Asymptotically Optimal Procedures for Sequential Joint Detection and Estimation
We investigate the problem of jointly testing multiple hypotheses and estimating a random parameter of the underlying distribution in a sequential setup.
Sparsity-aware Robust Community Detection(SPARCODE)
We compare the performance to popular graph and cluster-based community detection approaches on a variety of benchmark network and cluster analysis data sets.
Doppler Radar for the Extraction of Biomechanical Parameters in Gait Analysis
Further, a new method to extract individual leg flight times from radar data is introduced.
Bayesian Sequential Joint Detection and Estimation under Multiple Hypotheses
This problem is investigated in a sequential setup under mild assumptions on the underlying random process.
Decentralized Decision-Making Over Multi-Task Networks
In important applications involving multi-task networks with multiple objectives, agents in the network need to decide between these multiple objectives and reach an agreement about which single objective to follow for the network.
Robust Bayesian Cluster Enumeration Based on the $t$ Distribution
Hence, we propose a two-step cluster enumeration algorithm that uses the expectation maximization algorithm to partition the data and estimate cluster parameters prior to the calculation of one of the robust criteria.
Bayesian Sequential Joint Detection and Estimation
We formulate an unconstrained sequential decision problem, whose cost function is the weighted sum of the expected run-length and the detection/estimation errors.
Signal Processing
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling
Advances in the field of inverse reinforcement learning (IRL) have led to sophisticated inference frameworks that relax the original modeling assumption of observing an agent behavior that reflects only a single intention.
Bayesian Cluster Enumeration Criterion for Unsupervised Learning
We derive a new Bayesian Information Criterion (BIC) by formulating the problem of estimating the number of clusters in an observed data set as maximization of the posterior probability of the candidate models.
Gravitational Clustering: A Simple, Robust and Adaptive Approach for Distributed Networks
The cluster enumeration exploits the fact that the highest attraction on the mobile mass units is exerted by regions with a high density of feature vectors, i. e., gravitational clusters.
Computationally efficient heart rate estimation during physical exercise using photoplethysmographic signals
Wearable devices that acquire photoplethysmographic (PPG) signals are becoming increasingly popular to monitor the heart rate during physical exercise.
Ranked #5 on
Heart rate estimation
on WESAD
Bayesian Nonparametric Feature and Policy Learning for Decision-Making
Therefore, we propose a generative model for the states and actions.
Bayesian Nonparametric Unmixing of Hyperspectral Images
In this work, we propose a Bayesian nonparametric framework that jointly estimates the number of endmembers, the endmembers itself, and their abundances, by making use of the Indian Buffet Process as a prior for the endmembers.
Decentralized Clustering and Linking by Networked Agents
We consider the problem of decentralized clustering and estimation over multi-task networks, where agents infer and track different models of interest.
Dictionary Learning Strategies for Compressed Fiber Sensing Using a Probabilistic Sparse Model
We present a sparse estimation and dictionary learning framework for compressed fiber sensing based on a probabilistic hierarchical sparse model.
A Bayesian Approach to Policy Recognition and State Representation Learning
Learning from demonstration (LfD) is the process of building behavioral models of a task from demonstrations provided by an expert.
Inverse Reinforcement Learning in Swarm Systems
Inverse reinforcement learning (IRL) has become a useful tool for learning behavioral models from demonstration data.
