activity
20092022
most citedDecentralized Gaussian Filters for Cooperative Self-localization and Multi-target Tracking

58 citations · 176 across the 35 of their papers we have counts for

collaborators
Showing 2020Show all

12 papers · 1 filter

stat.ML20204 cited

Zeroth-Order Hybrid Gradient Descent: Towards A Principled Black-Box Optimization Framework

Pranay Sharma, Kaidi Xu, Sijia Liu +3

In this work, we focus on the study of stochastic zeroth-order (ZO) optimization which does not require first-order gradient information and uses only function evaluations. The pro…

cs.LG2020

Decentralized Federated Learning via Mutual Knowledge Transfer

Chengxi Li, Gang Li, Pramod K. Varshney

In this paper, we investigate the problem of decentralized federated learning (DFL) in Internet of things (IoT) systems, where a number of IoT clients train models collectively for…

cs.IT2020

On Performance Comparison of Multi-Antenna HD-NOMA, SCMA and PD-NOMA Schemes

Animesh Yadav, Chen Quan, Pramod K. Varshney +1

In this paper, we study the uplink channel throughput performance of a proposed novel multiple-antenna hybrid-domain non-orthogonal multiple access (MA-HD-NOMA) scheme. This scheme…

eess.SP2020

Joint Collaboration and Compression Design for Distributed Sequential Estimation in a Wireless Sensor Network

Xiancheng Cheng, Prashant Khanduri, Boxiao Chen +1

In this work, we propose a joint collaboration-compression framework for sequential estimation of a random vector parameter in a resource constrained wireless sensor network (WSN).…

eess.SP2020

A Novel Spectrally-Efficient Uplink Hybrid-Domain NOMA System

Chen Quan, Animesh Yadav, Baocheng Geng +2

This paper proposes a novel hybrid-domain (HD) non-orthogonal multiple access (NOMA) approach to support a larger number of uplink users than the recently proposed code-domain NOMA…

cs.LG202019 cited

A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning

Sijia Liu, Pin-Yu Chen, Bhavya Kailkhura +3

Zeroth-order (ZO) optimization is a subset of gradient-free optimization that emerges in many signal processing and machine learning applications. It is used for solving optimizati…