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Found 16 papers, 2 papers with code
Convergence of Expectation-Maximization Algorithm with Mixed-Integer Optimization
The convergence of expectation-maximization (EM)-based algorithms typically requires continuity of the likelihood function with respect to all the unknown parameters (optimization variables).
Joint State and Input Estimation for Linear Dynamical Systems with Sparse Control
We present a Bayesian approach that exploits the input sparsity to significantly improve estimation accuracy.
Anomaly Detection via Learning-Based Sequential Controlled Sensing
Our objective is to design a sequential selection policy that dynamically determines which processes to observe at each time with the goal to minimize the delay in making the decision and the total sensing cost.
Minimal Input Structural Modifications for Strongly Structural Controllability
This paper studies the problem of modifying the input matrix of a structured system to make the system strongly structurally controllable.
Sparse Millimeter Wave Channel Estimation From Partially Coherent Measurements
Our method exploits the sparse structure in mmWave channels for low training overhead and accounts for the phase errors in the channel measurements due to phase noise at the oscillator.
Kronecker-structured Sparse Vector Recovery with Application to IRS-MIMO Channel Estimation
The prior art only exploits the Kronecker structure in the support of the sparse vector and solves the entire linear system together leading to high computational complexity.
Bayesian Algorithms for Kronecker-structured Sparse Vector Recovery With Application to IRS-MIMO Channel Estimation
We study the sparse recovery problem with an underdetermined linear system characterized by a Kronecker-structured dictionary and a Kronecker-supported sparse vector.
Structure-aware Sparse Bayesian Learning-based Channel Estimation for Intelligent Reflecting Surface-aided MIMO
This paper presents novel cascaded channel estimation techniques for an intelligent reflecting surface-aided multiple-input multiple-output system.
Near-field focusing using phased arrays with dynamic polarization control
Phased arrays in near-field communication allow the transmitter to focus wireless signals in a small region around the receiver.
Temporal Detection of Anomalies via Actor-Critic Based Controlled Sensing
Based on the received observations, the decisionmaker first determines whether to declare that the number of anomalies has exceeded the threshold or to continue taking observations.
Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing
In this setting, we develop an anomaly detection algorithm that chooses the processes to be observed at a given time instant, decides when to stop taking observations, and declares the decision on anomalous processes.
LEARNING DISTRIBUTIONS GENERATED BY SINGLE-LAYER RELU NETWORKS IN THE PRESENCE OF ARBITRARY OUTLIERS
Further, we prove that ${O}(\frac{1}{\epsilon p^4}\log\frac{d}{\delta})$ samples are sufficient for our algorithm to estimate the NN parameters within an error of $\epsilon$ with probability $1-\delta$ when the probability of a sample being uncorrupted is $p$ and the problem dimension is $d$.
Anomaly Detection via Controlled Sensing and Deep Active Inference
In this paper, we address the anomaly detection problem where the objective is to find the anomalous processes among a given set of processes.
A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing
In this setting, we develop an anomaly detection algorithm that chooses the process to be observed at a given time instant, decides when to stop taking observations, and makes a decision regarding the anomalous processes.
Anomaly Detection Under Controlled Sensing Using Actor-Critic Reinforcement Learning
Our objective is to design a sequential sensor selection policy that dynamically determines which processes to observe at each time and when to terminate the detection algorithm.
Output Controllability of a Linear Dynamical System with Sparse Controls
In this paper, we study the conditions to be satisfied by a discrete-time linear system to ensure output controllability using sparse control inputs.
