AIConfPaper
← Search

H. Vincent Poor

44 accepted papers

2024

Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling

Arman Adibi, Nicolò Dal Fabbro, Luca Schenato, Sanjeev Kulkarni, H. Vincent Poor, George J. Pappas +2
AISTATS 2024poster

Motivated by applications in large-scale and multi-agent reinforcement learning, we study the non-asymptotic performance of stochastic approximation (SA) schemes with delayed updates under Markovian sampling. While the effect of delays has been extensively studied for optimization, the manner in whi…

Cited by 14Source ↗PDF ↗Scholar ↗
2023

Alternating Differentiation for Optimization Layers

Haixiang Sun, Ye Shi, Jingya Wang, Hoang Duong Tuan, H. Vincent Poor, Dacheng Tao
ICLR 2023poster

The idea of embedding optimization problems into deep neural networks as optimization layers to encode constraints and inductive priors has taken hold in recent years. Most existing methods focus on implicitly differentiating Karush–Kuhn–Tucker (KKT) conditions in a way that requires expensive compu…

Cited by 23Source ↗PDF ↗code ↗Scholar ↗
2023

Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations

Minshuo Chen, Yu Bai, H. Vincent Poor, Mengdi Wang
NeurIPS 2023poster

In real-world reinforcement learning (RL) systems, various forms of {\it impaired observability} can complicate matters. These situations arise when an agent is unable to observe the most recent state of the system due to latency or lossy channels, yet the agent must still make real-time decisions.…

Cited by 9Source ↗PDF ↗Scholar ↗
2023

Higher-Order Spatio-Temporal Neural Networks for Covid-19 Forecasting

Yuzhou Chen, Sotiris Batsakis, H. Vincent Poor
ICASSP 2023accepted

Coronavirus Disease 2019 (COVID-19) pneumonia started in December 2019 and cases have been reported in 240 countries/regions with more than 570 million confirmed cases and more than 6 million deaths which caused large casualties and huge economic losses. To enhance the understanding of the levels of…

Cited by 0Source ↗Scholar ↗
2022

A Dimensionality Reduction Method for Finding Least Favorable Priors with a Focus on Bregman Divergence

Alex R. Dytso, Mario Goldenbaum, H. Vincent Poor, Shlomo Shamai
AISTATS 2022poster

A common way of characterizing minimax estimators in point estimation is by moving the problem into the Bayesian estimation domain and finding a least favorable prior distribution. The Bayesian estimator induced by a least favorable prior, under mild conditions, is then known to be minimax. However,…

Cited by 0Source ↗PDF ↗Scholar ↗
2022

Competitive Multi-Agent Reinforcement Learning with Self-Supervised Representation

DiJia Su, Jason D. Lee, John M. Mulvey, H. Vincent Poor
ICASSP 2022accepted

We present MASRL: Competitive Multi-Agent Self-supervised representations for Reinforcement Learning in the multi-agent competitive environment. MASRL introduces a simple but effective self-supervised task: predicting a learning agent’s opponent’s future move. In doing this, the agent learns a stron…

Cited by 0Source ↗Scholar ↗
2022

Federated Stochastic Gradient Descent Begets Self-Induced Momentum

Howard H. Yang, Zuozhu Liu, Yaru Fu, Tony Q. S. Quek, H. Vincent Poor
ICASSP 2022accepted

Federated learning (FL) is an emerging machine learning method that can be applied in mobile edge systems, in which a server and a host of clients collaboratively train a statistical model utilizing the data and computation resources of the clients without directly exposing their privacy-sensitive d…

Cited by 0Source ↗Scholar ↗
2022

Performance Optimization for Wireless Semantic Communications over Energy Harvesting Networks

Mingzhe Chen, Yining Wang, H. Vincent Poor
ICASSP 2022accepted

In this paper, the optimization of semantic communications over energy harvesting networks is studied. In the considered model, a set of users use semantic communication techniques and the harvested energy to transmit text data to a base station (BS). Here, semantic communication techniques enable e…

Cited by 0Source ↗Scholar ↗
2022

Time-Conditioned Dances with Simplicial Complexes: Zigzag Filtration Curve based Supra-Hodge Convolution Networks for Time-series Forecasting

Yuzhou Chen, Yulia Gel, H. Vincent Poor
NeurIPS 2022accept

Graph neural networks (GNNs) offer a new powerful alternative for multivariate time series forecasting, demonstrating remarkable success in a variety of spatio-temporal applications, from urban flow monitoring systems to health care informatics to financial analytics. Yet, such GNN models pre-domina…

Cited by 19Source ↗PDF ↗Scholar ↗
2021

Communication Over Block Fading Channels - An Algorithmic Perspective On Optimal Transmission Schemes

Holger Boche, Rafael F. Schaefer, H. Vincent Poor
ICASSP 2021accepted

Wireless channels are considered that change over time but remain constant for a certain (coherence) period. This behavior is perfectly captured by block fading channels and affects the performance of the corresponding wireless communication systems. Desired closed-form characterizations of optimal…

Cited by 0Source ↗Scholar ↗
2021

DeHiB: Deep Hidden Backdoor Attack on Semi-supervised Learning via Adversarial Perturbation

Zhicong Yan, Gaolei Li, Yuan TIan, Jun Wu, Shenghong Li, Mingzhe Chen +1
AAAI 2021technical

The threat of data-poisoning backdoor attacks on learning algorithms typically comes from the labeled data. However, in deep semi-supervised learning (SSL), unknown threats mainly stem from the unlabeled data. In this paper, we propose a novel deep hidden backdoor (DeHiB) attack scheme for SSL-based…

Cited by 51Source ↗PDF ↗Scholar ↗
2021

Energy Minimization for Federated Learning with IRS-Assisted Over-the-Air Computation

Yuntao Hu, Ming Chen, Mingzhe Chen, Zhaohui Yang, Mohammad Shikh-Bahaei, H. Vincent Poor +1
ICASSP 2021accepted

This paper investigates the deployment of federated learning (FL) over an over-the-air computation (AirComp) and intelligent reflecting surface (IRS) based wireless network. In the considered system, devices transmit locally trained machine learning (ML) models to the base station (BS) which aggrega…

Cited by 0Source ↗Scholar ↗
2021

Leveraging A Multiple-Strain Model with Mutations in Analyzing the Spread of Covid-19

Anirudh Sridhar, Osman Yagan, Rashad Eletreby, Simon A. Levin, Joshua B. Plotkin, H. Vincent Poor
ICASSP 2021accepted

The spread of COVID-19 has been among the most devastating events affecting the health and well-being of humans worldwide since World War II. A key scientific goal concerning COVID-19 is to develop mathematical models that help us to understand and predict its spreading behavior, as well as to provi…

Cited by 0Source ↗Scholar ↗
2021

Neural Layered Min-Sum Decoding for Protograph LDPC Codes

Dexin Zhang, Jincheng Dai, Kailin Tan, Kai Niu, Mingzhe Chen, H. Vincent Poor +1
ICASSP 2021accepted

In this paper, layered min-sum (MS) iterative decoding is formulated as a customized neural network following the sequential scheduling of check node (CN) updates. By virtue of the lifting structure of protograph low-density parity-check (LDPC) codes, identical network parameters are shared among al…

Cited by 0Source ↗Scholar ↗
2021

Real Number Signal Processing can Detect Denial-of-Service Attacks

Holger Boche, Rafael F. Schaefer, H. Vincent Poor
ICASSP 2021accepted

Wireless communication systems are inherently vulnerable to adversarial attacks since malevolent jammers might jam and disrupt the legitimate transmission intentionally. Of particular interest are so- called denial-of-service (DoS) attacks in which the jammer is able to completely disrupt the commun…

Cited by 7Source ↗Scholar ↗
2021

Spatial Equalization Before Reception: Reconfigurable Intelligent Surfaces for Multi-Path Mitigation

Hongliang Zhang, Lingyang Song, Zhu Han, H. Vincent Poor
ICASSP 2021accepted

Reconfigurable intelligent surfaces (RISs), which enable tunable anomalous reflection, have appeared as a promising method to enhance wireless systems. In this paper, we propose to use an RIS as a spatial equalizer to address the well-known multi-path fading phenomenon. By introducing some controlla…

Cited by 0Source ↗Scholar ↗
2020

A Switching Transmission Game with Latency as the User's Communication Utility

Andrey Garnaev, Athina P. Petropulu, Wade Trappe, H. Vincent Poor
ICASSP 2020accepted

We consider the communication between a source (user) and a destination in the presence of a jammer, and study resource assignment in a non-cooperative game theory framework using communication latency as the user's utility. The user switches between two different modes, i.e., the (a) regular transm…

Cited by 0Source ↗Scholar ↗
2020

Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks

Howard H. Yang, Ahmed Arafa, Tony Q. S. Quek, H. Vincent Poor
ICASSP 2020accepted

Federated learning (FL) is a machine learning model that preserves data privacy in the training process. Specifically, FL brings the model directly to the user equipments (UEs) for local training, where an edge server periodically collects the trained parameters to produce an improved model and send…

Cited by 0Source ↗Scholar ↗
2020

Bayesian Multiple Change-Point Detection with Limited Communication

Topi Halme, Eyal Nitzan, H. Vincent Poor, Visa Koivunen
ICASSP 2020accepted

Several modern applications involve large-scale sensor networks for statistical inference. For example, such sensor networks are of significant interest for Internet of Things applications. In this paper, we consider Bayesian multiple changepoint detection using a sensor network in which a fusion ce…

Cited by 0Source ↗Scholar ↗
2020

Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters

Kaiyi Ji, Jason Lee, Yingbin Liang, H. Vincent Poor
NeurIPS 2020poster

Although model-agnostic meta-learning (MAML) is a very successful algorithm in meta-learning practice, it can have high computational cost because it updates all model parameters over both the inner loop of task-specific adaptation and the outer-loop of meta initialization training. A more efficient…

Cited by 91Source ↗PDF ↗Scholar ↗
2020

Federated Learning with Quantization Constraints

Nir Shlezinger, Mingzhe Chen, Yonina C. Eldar, H. Vincent Poor, Shuguang Cui
ICASSP 2020accepted

Traditional deep learning models are trained on centralized servers using labeled sample data collected from edge devices. This data often includes private information, which the users may not be willing to share. Federated learning (FL) is an emerging approach to train such learning models without…

Cited by 0Source ↗Scholar ↗
2020

Hybrid Precoding for Secure Transmission in Reflect-Array-Assisted Massive MIMO Systems

Saba Asaad, Rafael F. Schaefer, H. Vincent Poor
ICASSP 2020accepted

Recently, a hybrid analog-digital architecture has been proposed for multiuser MIMO transmission in the millimeter-wave spectrum using reflect-arrays. The architecture exhibits scalability and high energy-efficiency while keeping the transmitter cost-efficient. Inspired by this architecture, we desi…

Cited by 0Source ↗Scholar ↗
2020

Latency-Minimized Design of secure transmissions in UAV-Aided Communications

Xiongwei Wu, Qiang Li, Yawei Lu, H. Vincent Poor, Victor C. M. Leung, P. C. Ching
ICASSP 2020accepted

Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations to provide communication service for remote mobile users due to their high mobility and flexible deployment. However, the line-of-sight (LoS) wireless links are vulnerable to be intercepted by the eavesdropper (Eve), which prese…

Cited by 0Source ↗Scholar ↗
2020

On Distributed Stochastic Gradient Algorithms for Global Optimization

Brian Swenson, Anirudh Sridhar, H. Vincent Poor
ICASSP 2020accepted

The paper considers the problem of network-based computation of global minima in smooth nonconvex optimization problems. It is known that distributed gradient-descent-type algorithms can achieve convergence to the set of global minima by adding slowly decaying Gaussian noise in order to escape local…

Cited by 0Source ↗Scholar ↗
2020

On Polar Coding For Finite Blocklength Secret Key Generation Over Wireless Channels

Henri Hentilä, Yanina Y. Shkel, Visa Koivunen, H. Vincent Poor
ICASSP 2020accepted

We consider the problem of secret key generation from correlated Gaussian random variables in the finite blocklength regime. Such keys could be used to encrypt communication in IoT networks, and have provable secrecy guarantees in contrast to classic cryptographic approaches. We investigate the perf…

Cited by 0Source ↗Scholar ↗
2020

Robust Transmission Over Channels with Channel Uncertainty: an Algorithmic Perspective

Holger Boche, Rafael F. Schaefer, H. Vincent Poor
ICASSP 2020accepted

The availability and quality of channel state information heavily influences the performance of wireless communication systems. For perfect channel knowledge, optimal signal processing and coding schemes are well studied and often closed-form solutions are known. On the other hand, the case of imper…

Cited by 6Source ↗Scholar ↗
2020

Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization

Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, H. Vincent Poor
NeurIPS 2020poster

In federated learning, heterogeneity in the clients' local datasets and computation speeds results in large variations in the number of local updates performed by each client in each communication round. Naive weighted aggregation of such models causes objective inconsistency, that is, the global mo…

Cited by 1715Source ↗PDF ↗code ↗Scholar ↗
2020

Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality

Changxiao Cai, H. Vincent Poor, Yuxin Chen
ICML 2020poster

We study the distribution and uncertainty of nonconvex optimization for noisy tensor completion — the problem of estimating a low-rank tensor given incomplete and corrupted observations of its entries. Focusing on a two-stage nonconvex estimation algorithm proposed by (Cai et al., 2019), we characte…

Cited by 28Source ↗PDF ↗Scholar ↗
2019

Detectability of Denial-of-service Attacks on Communication Systems

Holger Boche, Rafael F. Schaefer, H. Vincent Poor
ICASSP 2019accepted

Wireless communication systems are inherently vulnerable to adversarial attacks since malevolent jammers might jam and disrupt the legitimate transmission intentionally. Accordingly it is of crucial interest for the legitimate users to detect such adversarial attacks. This paper develops a detection…

Cited by 0Source ↗Scholar ↗
2019

On the Computability of the Secret Key Capacity under Rate Constraints

Holger Boche, Rafael F. Schaefer, H. Vincent Poor
ICASSP 2019accepted

Secret key generation refers to the problem of generating a common secret key without revealing any information about it to an eaves-dropper. All users observe correlated components of a common source and can further use a rate-limited public channel for discussion which is open to eavesdroppers. Th…

Cited by 0Source ↗Scholar ↗
2018

On the Equivalence of $f$-Divergence Balls and Density Bands in Robust Detection

Michael Fauß, Abdelhak M. Zoubir, H. Vincent Poor
ICASSP 2018accepted

The paper deals with minimax optimal statistical tests for two composite hypotheses, where each hypothesis is defined by a nonparametric uncertainty set of feasible distributions. It is shown that for every pair of uncertainty sets of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xli…

Cited by 0Source ↗Scholar ↗
2017

Sequential joint signal detection and signal-to-noise ratio estimation

Michael Fauss, Kyatsandra G. Nagananda, Abdelhak M. Zoubir, H. Vincent Poor
ICASSP 2017accepted

The sequential analysis of the problem of joint signal detection and signal-to-noise ratio (SNR) estimation for a linear Gaussian observation model is considered. The problem is posed as an optimization setup where the goal is to minimize the number of samples required to achieve the desired (i) typ…

Cited by 0Source ↗Scholar ↗
2017

Two-dimensional anti-jamming communication based on deep reinforcement learning

Guoan Han, Liang Xiao, H. Vincent Poor
ICASSP 2017accepted

In this paper, a two-dimensional anti-jamming communication scheme for cognitive radio networks is developed, in which a secondary user (SU) exploits both spread spectrum and user mobility to address jamming attacks, while not interfering with primary users. By applying a deep Q-network algorithm, t…

Cited by 0Source ↗Scholar ↗
2016

Adaptive distributed compressed estimation based on recursive least squares with sensing matrix design

Huang Bai, Songcen Xu, Sheng Li, Rodrigo C. de Lamare, Xiongxiong He, H. Vincent Poor
ICASSP 2016accepted

In this paper, a distributed compressed estimation (DCE) scheme is presented based on a distributed recursive-least squares algorithm for sparse signals and systems along with a sensing matrix design procedure based on compressive sensing techniques. The D-CE scheme consists of compression and decom…

Cited by 0Source ↗Scholar ↗
2016

Nonparametric detection of an anomalous disk over a two-dimensional lattice network

Shaofeng Zou, Yingbin Liang, H. Vincent Poor
ICASSP 2016accepted

Nonparametric detection of existence of an anomalous disk over a lattice network is investigated. If an anomalous disk exists, then all nodes belonging to the disk observe samples generated by a distribution q, whereas all other nodes observe samples generated by a distribution p that is distinct fr…

Cited by 0Source ↗Scholar ↗