Search Results for author: Pramod K. Varshney

Found 34 papers, 0 papers with code

Efficient Ordered-Transmission Based Distributed Detection under Data Falsification Attacks

no code implementations18 Jul 2022 Chen Quan, Nandan Sriranga, Haodong Yang, Yunghsiang S. Han, Baocheng Geng, Pramod K. Varshney

In distributed detection systems, energy-efficient ordered transmission (EEOT) schemes are able to reduce the number of transmissions required to make a final decision.

Distributed Estimation in Large Scale Wireless Sensor Networks via a Two Step Group-based Approach

no code implementations17 Mar 2022 Shan Zhang, Pranay Sharma, Baocheng Geng, Pramod K. Varshney

To achieve greater sensor transmission and estimation efficiency, we propose a two step group-based collaborative distributed estimation scheme, where in the first step, sensors form dependence driven groups such that sensors in the same group are highly dependent, while sensors from different groups are independent, and perform a copula-based maximum a posteriori probability (MAP) estimation via intragroup collaboration.

Federated Minimax Optimization: Improved Convergence Analyses and Algorithms

no code implementations9 Mar 2022 Pranay Sharma, Rohan Panda, Gauri Joshi, Pramod K. Varshney

In this paper, we consider nonconvex minimax optimization, which is gaining prominence in many modern machine learning applications such as GANs.

Distributed Optimization Federated Learning

Ordered Transmission-based Detection in Distributed Networks in the Presence of Byzantines

no code implementations21 Jan 2022 Chen Quan, Saikiran Bulusu, Baocheng Geng, Pramod K. Varshney

The ordered transmission (OT) scheme reduces the number of transmissions needed in the network to make the final decision, while it maintains the same probability of error as the system without using OT scheme.

Monitoring and Anomaly Detection Actor-Critic Based Controlled Sensing

no code implementations3 Jan 2022 Geethu Joseph, M. Cenk Gursoy, Pramod K. Varshney

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.

Anomaly Detection Decision Making

Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing

no code implementations8 Dec 2021 Geethu Joseph, Chen Zhong, M. Cenk Gursoy, Senem Velipasalar, Pramod K. Varshney

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.

Anomaly Detection Decision Making

Multi-sensor Joint Adaptive Birth Sampler for Labeled Random Finite Set Tracking

no code implementations11 Aug 2021 Anthony Trezza, Donald J. Bucci Jr., Pramod K. Varshney

A naive construction of the multi-sensor measurement adaptive birth set distribution leads to an exponential number of newborn components in the number of sensors.

STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning

no code implementations NeurIPS 2021 Prashant Khanduri, Pranay Sharma, Haibo Yang, Mingyi Hong, Jia Liu, Ketan Rajawat, Pramod K. Varshney

Despite extensive research, for a generic non-convex FL problem, it is not clear, how to choose the WNs' and the server's update directions, the minibatch sizes, and the local update frequency, so that the WNs use the minimum number of samples and communication rounds to achieve the desired solution.

Federated Learning

A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing

no code implementations12 May 2021 Geethu Joseph, M. Cenk Gursoy, Pramod K. Varshney

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 Decision Making

Anomaly Detection via Controlled Sensing and Deep Active Inference

no code implementations12 May 2021 Geethu Joseph, Chen Zhong, M. Cenk Gursoy, Senem Velipasalar, Pramod K. Varshney

In this paper, we address the anomaly detection problem where the objective is to find the anomalous processes among a given set of processes.

Anomaly Detection Decision Making

Decentralized Federated Learning via Mutual Knowledge Transfer

no code implementations24 Dec 2020 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 a common task without sharing their private training data in the absence of a central server.

Federated Learning Transfer Learning

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

no code implementations21 Dec 2020 Pranay Sharma, Kaidi Xu, Sijia Liu, Pin-Yu Chen, Xue Lin, Pramod K. Varshney

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.

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

no code implementations6 Oct 2020 Xiancheng Cheng, Prashant Khanduri, Boxiao Chen, Pramod K. Varshney

We propose two versions of compression design, one centralized where the compression strategies are derived at the FC and the other decentralized, where the local sensors compute their individual compression matrices independently.

A Novel Spectrally-Efficient Uplink Hybrid-Domain NOMA System

no code implementations17 Jul 2020 Chen Quan, Animesh Yadav, Baocheng Geng, Pramod K. Varshney, H. Vincent Poor

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 approach, i. e., sparse code multiple access (SCMA).

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

no code implementations11 Jun 2020 Sijia Liu, Pin-Yu Chen, Bhavya Kailkhura, Gaoyuan Zhang, Alfred Hero, Pramod K. Varshney

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

Machine Learning Management

Anomaly Detection Under Controlled Sensing Using Actor-Critic Reinforcement Learning

no code implementations26 May 2020 Geethu Joseph, M. Cenk Gursoy, Pramod K. Varshney

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.

Anomaly Detection Decision Making +2

Distributed Stochastic Non-Convex Optimization: Momentum-Based Variance Reduction

no code implementations1 May 2020 Prashant Khanduri, Pranay Sharma, Swatantra Kafle, Saikiran Bulusu, Ketan Rajawat, Pramod K. Varshney

In this work, we propose a distributed algorithm for stochastic non-convex optimization.

Optimization and Control Distributed, Parallel, and Cluster Computing

Anomalous Example Detection in Deep Learning: A Survey

no code implementations16 Mar 2020 Saikiran Bulusu, Bhavya Kailkhura, Bo Li, Pramod K. Varshney, Dawn Song

This survey tries to provide a structured and comprehensive overview of the research on anomaly detection for DL based applications.

Anomaly Detection

Prospect Theory Based Crowdsourcing for Classification in the Presence of Spammers

no code implementations3 Sep 2019 Baocheng Geng, Qunwei Li, Pramod K. Varshney

We consider the $M$-ary classification problem via crowdsourcing, where crowd workers respond to simple binary questions and the answers are aggregated via decision fusion.

General Classification

K-medoids Clustering of Data Sequences with Composite Distributions

no code implementations31 Jul 2018 Tiexing Wang, Qunwei Li, Donald J. Bucci, Yingbin Liang, Biao Chen, Pramod K. Varshney

In particular, the error exponent is characterized when either the Kolmogrov-Smirnov distance or the maximum mean discrepancy are used as the distance metric.

Why Interpretability in Machine Learning? An Answer Using Distributed Detection and Data Fusion Theory

no code implementations25 Jun 2018 Kush R. Varshney, Prashant Khanduri, Pranay Sharma, Shan Zhang, Pramod K. Varshney

Such arguments, however, fail to acknowledge that the overall decision-making system is composed of two entities: the learned model and a human who fuses together model outputs with his or her own information.

Decision Making Machine Learning

Decision Tree Design for Classification in Crowdsourcing Systems

no code implementations1 May 2018 Baocheng Geng, Qunwei Li, Pramod K. Varshney

In this paper, we present a novel sequential paradigm for classification in crowdsourcing systems.

Classification General Classification

Human-Machine Inference Networks For Smart Decision Making: Opportunities and Challenges

no code implementations29 Jan 2018 Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney

The emerging paradigm of Human-Machine Inference Networks (HuMaINs) combines complementary cognitive strengths of humans and machines in an intelligent manner to tackle various inference tasks and achieves higher performance than either humans or machines by themselves.

Decision Making Machine Learning

A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms

no code implementations16 Dec 2017 Bhavya Kailkhura, Jayaraman J. Thiagarajan, Charvi Rastogi, Pramod K. Varshney, Peer-Timo Bremer

Third, we propose an efficient estimator to evaluate the space-filling properties of sample designs in arbitrary dimensions and use it to develop an optimization framework to generate high quality space-filling designs.

Image Reconstruction

A Memristor-Based Optimization Framework for AI Applications

no code implementations18 Oct 2017 Sijia Liu, Yanzhi Wang, Makan Fardad, Pramod K. Varshney

In addition to ADMM, implementation of a customized power iteration (PI) method for eigenvalue/eigenvector computation using memristor crossbars is discussed.

Machine Learning

Robust Decentralized Learning Using ADMM with Unreliable Agents

no code implementations14 Oct 2017 Qunwei Li, Bhavya Kailkhura, Ryan Goldhahn, Priyadip Ray, Pramod K. Varshney

We also provide conditions on the erroneous updates for exact convergence to the optimal solution.

Machine Learning

Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization

no code implementations ICML 2017 Qunwei Li, Yi Zhou, Yingbin Liang, Pramod K. Varshney

Then, by exploiting the Kurdyka-{\L}ojasiewicz (\KL) property for a broad class of functions, we establish the linear and sub-linear convergence rates of the function value sequence generated by APGnc.

Towards the Design of Prospect-Theory based Human Decision Rules for Hypothesis Testing

no code implementations4 Oct 2016 V. Sriram Siddhardh Nadendla, Swastik Brahma, Pramod K. Varshney

Detection rules have traditionally been designed for rational agents that minimize the Bayes risk (average decision cost).

Two-sample testing

Multi-object Classification via Crowdsourcing with a Reject Option

no code implementations1 Feb 2016 Qunwei Li, Aditya Vempaty, Lav R. Varshney, Pramod K. Varshney

We present an aggregation approach using a weighted majority voting rule, where each worker's response is assigned an optimized weight to maximize the crowd's classification performance.

Classification General Classification

Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach

no code implementations22 Jan 2016 Prashant Khanduri, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Pramod K. Varshney

This paper considers the problem of high dimensional signal detection in a large distributed network whose nodes can collaborate with their one-hop neighboring nodes (spatial collaboration).

Sparse Learning

Consensus based Detection in the Presence of Data Falsification Attacks

no code implementations14 Apr 2015 Bhavya Kailkhura, Swastik Brahma, Pramod K. Varshney

This paper considers the problem of detection in distributed networks in the presence of data falsification (Byzantine) attacks.

Hybrid Maximum Likelihood Modulation Classification Using Multiple Radios

no code implementations4 Mar 2013 Onur Ozdemir, Ruoyu Li, Pramod K. Varshney

The performance of a modulation classifier is highly sensitive to channel signal-to-noise ratio (SNR).

Classification General Classification

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