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05/25/2023
Representation Transfer Learning via Multiple Pre-trained models for Linear Regression
In this paper, we consider the problem of learning a linear regression m...
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05/10/2023
Common Information Dimension
The exact common information between a set of random variables X_1,...,X...
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02/22/2023
Multi-Message Shuffled Privacy in Federated Learning
We study differentially private distributed optimization under communica...
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01/10/2023
HQAlign: Aligning nanopore reads for SV detection using current-level modeling
Motivation: Detection of structural variants (SV) from the alignment of ...
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07/07/2022
Differentially Private Stochastic Linear Bandits: (Almost) for Free
In this paper, we propose differentially private algorithms for the prob...
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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...
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07/01/2022
On Leave-One-Out Conditional Mutual Information For Generalization
We derive information theoretic generalization bounds for supervised lea...
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01/28/2022
Improving Group Testing via Gradient Descent
We study the problem of group testing with non-identical, independent pr...
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12/23/2021
Decentralized Multi-Task Stochastic Optimization With Compressed Communications
We consider a multi-agent network where each node has a stochastic (loca...
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07/29/2021
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning
Traditionally, federated learning (FL) aims to train a single global mod...
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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...
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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...
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05/11/2021
On the Renyi Differential Privacy of the Shuffle Model
The central question studied in this paper is Renyi Differential Privacy...
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02/23/2021
QuPeL: Quantized Personalization with Applications to Federated Learning
Traditionally, federated learning (FL) aims to train a single global mod...
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12/14/2020
Quantizing data for distributed learning
We consider machine learning applications that train a model by leveragi...
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12/04/2020
Group testing for overlapping communities
In this paper, we propose algorithms that leverage a known community str...
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08/17/2020
Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs
We consider a distributed empirical risk minimization (ERM) optimization...
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07/16/2020
Community aware group testing
Group testing pools together diagnostic samples to reduce the number of ...
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06/25/2020
Distortion based Light-weight Security for Cyber-Physical Systems
In Cyber-Physical Systems (CPS), inference based on communicated data is...
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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 ...
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06/01/2020
Coded Caching for Heterogeneous Wireless Networks
This chapter provides an overview of coded caching in the context of het...
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05/29/2020
Algorithms for reconstruction over single and multiple deletion channels
Recent advances in DNA sequencing technology and DNA storage systems hav...
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05/24/2020
Successive Refinement of Privacy
This work examines a novel question: how much randomness is needed to ac...
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05/16/2020
Byzantine-Resilient SGD in High Dimensions on Heterogeneous Data
We study distributed stochastic gradient descent (SGD) in the master-wor...
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05/13/2020
SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimization
In this paper, we consider the problem of communication-efficient decent...
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11/01/2019
On Distributed Quantization for Classification
We consider the problem of distributed feature quantization, where the g...
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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...
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07/05/2019
Data Encoding for Byzantine-Resilient Distributed Optimization
We study distributed optimization in the presence of Byzantine adversari...
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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...
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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...
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03/19/2019
Differentially Private Consensus-Based Distributed Optimization
Data privacy is an important concern in learning, when datasets contain ...
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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...
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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...
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09/12/2018
Distorting an Adversary's View in Cyber-Physical Systems
In Cyber-Physical Systems (CPSs), inference based on communicated data i...
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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 ...
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08/02/2018
Energy-Efficiency Gains of Caching for Interference Channels
This paper initiates the study of energy-efficiency gains provided by ca...
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03/22/2018
Leveraging mm-Wave Communication for Security
The fact that Millimeter Wave (mmWave) communication needs to be directi...
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03/22/2018
Leveraging Millimeter Waves for Security
The fact that Millimeter Wave (mmWave) communication needs to be directi...
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03/14/2018
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning
Performance of distributed optimization and learning systems is bottlene...
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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...
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11/14/2017
