b'Suhas Diggavi'

research

∙ 02/22/2023

Multi-Message Shuffled Privacy in Federated Learning

We study differentially private distributed optimization under communica...

0 Antonious M. Girgis, et al. ∙

research

∙ 01/10/2023

HQAlign: Aligning nanopore reads for SV detection using current-level modeling

Motivation: Detection of structural variants (SV) from the alignment of ...

0 Dhaivat Joshi, et al. ∙

research

∙ 07/07/2022

Differentially Private Stochastic Linear Bandits: (Almost) for Free

In this paper, we propose differentially private algorithms for the prob...

0 Osama A. Hanna, et al. ∙

research

∙ 07/05/2022

A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy

A distinguishing characteristic of federated learning is that the (local...

13 Kaan Ozkara, et al. ∙

research

∙ 07/01/2022

On Leave-One-Out Conditional Mutual Information For Generalization

We derive information theoretic generalization bounds for supervised lea...

8 Mohamad Rida Rammal, et al. ∙

research

∙ 01/28/2022

Improving Group Testing via Gradient Descent

We study the problem of group testing with non-identical, independent pr...

0 Sundara Rajan Srinivasavaradhan, et al. ∙

research

∙ 12/23/2021

Decentralized Multi-Task Stochastic Optimization With Compressed Communications

We consider a multi-agent network where each node has a stochastic (loca...

4 Navjot Singh, et al. ∙

research

∙ 07/29/2021

QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning

Traditionally, federated learning (FL) aims to train a single global mod...

1 Kaan Ozkara, et al. ∙

research

∙ 07/19/2021

Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learning

We study privacy in a distributed learning framework, where clients coll...

5 Antonious M. Girgis, et al. ∙

research

∙ 06/20/2021

Dynamic group testing to control and monitor disease progression in a population

In the context of a pandemic like COVID-19, and until most people are va...

0 Sundara Rajan Srinivasavaradhan, et al. ∙

research

∙ 05/11/2021

On the Renyi Differential Privacy of the Shuffle Model

The central question studied in this paper is Renyi Differential Privacy...

5 Antonious M. Girgis, et al. ∙

research

∙ 02/23/2021

QuPeL: Quantized Personalization with Applications to Federated Learning

Traditionally, federated learning (FL) aims to train a single global mod...

18 Kaan Ozkara, et al. ∙

research

∙ 12/14/2020

Quantizing data for distributed learning

We consider machine learning applications that train a model by leveragi...

9 Osama A. Hanna, et al. ∙

research

∙ 12/04/2020

Group testing for overlapping communities

In this paper, we propose algorithms that leverage a known community str...

0 Pavlos Nikolopoulos, et al. ∙

research

∙ 08/17/2020

Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs

We consider a distributed empirical risk minimization (ERM) optimization...

21 Antonious M. Girgis, et al. ∙

research

∙ 07/16/2020

Community aware group testing

Group testing pools together diagnostic samples to reduce the number of ...

0 Pavlos Nikolopoulos, et al. ∙

research

∙ 06/25/2020

Distortion based Light-weight Security for Cyber-Physical Systems

In Cyber-Physical Systems (CPS), inference based on communicated data is...

0 Gaurav Kumar Agarwal, et al. ∙

research

∙ 06/22/2020

Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data

We study stochastic gradient descent (SGD) with local iterations in the ...

0 Deepesh Data, et al. ∙

research

∙ 06/01/2020

Coded Caching for Heterogeneous Wireless Networks

This chapter provides an overview of coded caching in the context of het...

0 Jad Hachem, et al. ∙

research

∙ 05/29/2020

Algorithms for reconstruction over single and multiple deletion channels

Recent advances in DNA sequencing technology and DNA storage systems hav...

0 Sundara Rajan Srinivasavaradhan, et al. ∙

research

∙ 05/24/2020

Successive Refinement of Privacy

This work examines a novel question: how much randomness is needed to ac...

0 Antonious M. Girgis, et al. ∙

research

∙ 05/16/2020

Byzantine-Resilient SGD in High Dimensions on Heterogeneous Data

We study distributed stochastic gradient descent (SGD) in the master-wor...

7 Deepesh Data, et al. ∙

research

∙ 05/13/2020

SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimization

In this paper, we consider the problem of communication-efficient decent...

3 Navjot Singh, et al. ∙

research

∙ 11/01/2019

On Distributed Quantization for Classification

We consider the problem of distributed feature quantization, where the g...

5 Osama A. Hanna, et al. ∙

research

∙ 10/31/2019

SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Stochastic Optimization

In this paper, we propose and analyze SPARQ-SGD, which is an event-trigg...

11 Navjot Singh, et al. ∙

research

∙ 07/05/2019

Data Encoding for Byzantine-Resilient Distributed Optimization

We study distributed optimization in the presence of Byzantine adversari...

0 Deepesh Data, et al. ∙

research

∙ 06/06/2019

Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations

Communication bottleneck has been identified as a significant issue in d...

0 Debraj Basu, et al. ∙

research

∙ 04/03/2019

Securing State Estimation Under Sensor and Actuator Attacks: Theory and Design

This paper discusses the problem of estimating the state of a linear tim...

0 Mehrdad Showkatbakhsh, et al. ∙

research

∙ 03/19/2019

Differentially Private Consensus-Based Distributed Optimization

Data privacy is an important concern in learning, when datasets contain ...

14 Mehrdad Showkatbakhsh, et al. ∙

research

∙ 02/13/2019

Privacy-Utility Trade-off of Linear Regression under Random Projections and Additive Noise

Data privacy is an important concern in machine learning, and is fundame...

18 Mehrdad Showkatbakhsh, et al. ∙

research

∙ 12/09/2018

On the Generalized Degrees of Freedom of Noncoherent Interference Channel

We study the generalized degrees of freedom (gDoF) of the block-fading n...

0 Joyson Sebastian, et al. ∙

research

∙ 09/12/2018

Distorting an Adversary's View in Cyber-Physical Systems

In Cyber-Physical Systems (CPSs), inference based on communicated data i...

0 Gaurav Kumar Agarwal, et al. ∙

research

∙ 08/04/2018

Implementation and Analysis of Stable PUFs Using Gate Oxide Breakdown

We implement and analyze highly stable PUFs using two random gate oxide ...

0 Wei-Che Wang, et al. ∙

research

∙ 08/02/2018

Energy-Efficiency Gains of Caching for Interference Channels

This paper initiates the study of energy-efficiency gains provided by ca...

0 Jad Hachem, et al. ∙

research

∙ 03/22/2018

Leveraging mm-Wave Communication for Security

The fact that Millimeter Wave (mmWave) communication needs to be directi...

0 Mohammed Karmoose, et al. ∙

research

∙ 03/22/2018

Leveraging Millimeter Waves for Security

The fact that Millimeter Wave (mmWave) communication needs to be directi...

0 Mohammed Karmoose, et al. ∙

research

∙ 03/14/2018

Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning

Performance of distributed optimization and learning systems is bottlene...

0 Can Karakus, et al. ∙

research

∙ 02/07/2018

On Capacity of Non-Coherent Diamond Networks

There is a vast body of work on the capacity bounds for a "coherent" wir...

0 Joyson Sebastian, et al. ∙

research

∙ 11/14/2017

Straggler Mitigation in Distributed Optimization Through Data Encoding

Slow running or straggler tasks can significantly reduce computation spe...

0 Can Karakus, et al. ∙

Suhas Diggavi

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