Search Results for author: Ravi Tandon

Found 19 papers, 1 papers with code

Learning Fair Classifiers via Min-Max F-divergence Regularization

no code implementations28 Jun 2023 Meiyu Zhong, Ravi Tandon

In comparison to several existing approaches for learning fair classifiers (including pre-processing, post-processing and other regularization methods), we show that the proposed F-divergence based framework achieves state-of-the-art performance with respect to the trade-off between accuracy and fairness.

Decision Making Fairness

Generalization Bounds for Neural Belief Propagation Decoders

no code implementations17 May 2023 Sudarshan Adiga, Xin Xiao, Ravi Tandon, Bane Vasic, Tamal Bose

We present new theoretical results which bound this gap and show the dependence on the decoder complexity, in terms of code parameters (blocklength, message length, variable/check node degrees), decoding iterations, and the training dataset size.

Generalization Bounds

Differentially Private Community Detection for Stochastic Block Models

no code implementations31 Jan 2022 Mohamed Seif, Dung Nguyen, Anil Vullikanti, Ravi Tandon

To the best of our knowledge, this is the first work to study the impact of privacy constraints on the fundamental limits for community detection.

Community Detection Computational Efficiency +1

Unsupervised Change Detection using DRE-CUSUM

no code implementations27 Jan 2022 Sudarshan Adiga, Ravi Tandon

We present a theoretical justification as well as accuracy guarantees which show that the proposed statistic can reliably detect statistical changes, irrespective of the split point.

Change Detection Density Ratio Estimation +2

FAID Diversity via Neural Networks

no code implementations10 May 2021 Xin Xiao, Nithin Raveendran, Bane Vasic, Shu Lin, Ravi Tandon

Decoder diversity is a powerful error correction framework in which a collection of decoders collaboratively correct a set of error patterns otherwise uncorrectable by any individual decoder.

Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation

1 code implementation2 Mar 2021 Mohamed Seif, Wei-Ting Chang, Ravi Tandon

Specifically, the central DP privacy leakage has been shown to scale as $\mathcal{O}(1/K^{1/2})$, where $K$ is the number of users.

Federated Learning

Adversarial Filters for Secure Modulation Classification

no code implementations15 Aug 2020 Alex Berian, Kory Staab, Noel Teku, Gregory Ditzler, Tamal Bose, Ravi Tandon

This paper considers the problem of secure modulation classification, where a transmitter (Alice) wants to maximize MC accuracy at a legitimate receiver (Bob) while minimizing MC accuracy at an eavesdropper (Eve).

Classification General Classification

Asymmetric Leaky Private Information Retrieval

no code implementations4 Jun 2020 Islam Samy, Mohamed A. Attia, Ravi Tandon, Loukas Lazos

Such relaxation is relevant in applications where privacy can be traded for communication efficiency.

Information Retrieval Retrieval

Wireless Federated Learning with Local Differential Privacy

no code implementations12 Feb 2020 Mohamed Seif, Ravi Tandon, Ming Li

In this paper, we study the problem of federated learning (FL) over a wireless channel, modeled by a Gaussian multiple access channel (MAC), subject to local differential privacy (LDP) constraints.

Cryptography and Security Information Theory Information Theory

Communication Efficient Federated Learning over Multiple Access Channels

no code implementations23 Jan 2020 Wei-Ting Chang, Ravi Tandon

In particular, we focus on the design of digital gradient transmission schemes over a MAC, where gradients at each user are first quantized, and then transmitted over a MAC to be decoded individually at the PS.

Federated Learning Informativeness +1

Latent-variable Private Information Retrieval

no code implementations16 Jan 2020 Islam Samy, Mohamed A. Attia, Ravi Tandon, Loukas Lazos

To prevent such information leakage, the goal of classical PIR is to hide the identity of the content/message being accessed, which subsequently also hides the latent attributes.

Information Retrieval Retrieval

Random Sampling for Distributed Coded Matrix Multiplication

no code implementations16 May 2019 Wei-Ting Chang, Ravi Tandon

Such approximate schemes make use of randomization techniques to speed up the computation process.

Context-aware Data Aggregation with Localized Information Privacy

no code implementations6 Apr 2018 Bo Jiang, Ming Li, Ravi Tandon

The notion of context-awareness is incorporated in LIP by the introduction of priors, which enables the design of privacy-preserving data aggregation with knowledge of priors.

Privacy Preserving

Near Optimal Coded Data Shuffling for Distributed Learning

no code implementations5 Jan 2018 Mohamed A. Attia, Ravi Tandon

Data shuffling between distributed cluster of nodes is one of the critical steps in implementing large-scale learning algorithms.

On the Worst-case Communication Overhead for Distributed Data Shuffling

no code implementations30 Sep 2016 Mohamed Attia, Ravi Tandon

At each iteration over the data, it is common practice to randomly re-shuffle the data at the master node, assigning different batches for each worker to process.

Information Theoretic Limits of Data Shuffling for Distributed Learning

no code implementations16 Sep 2016 Mohamed Attia, Ravi Tandon

Data shuffling is one of the fundamental building blocks for distributed learning algorithms, that increases the statistical gain for each step of the learning process.

Hierarchical Quickest Change Detection via Surrogates

no code implementations31 Mar 2016 Prithwish Chakraborty, Sathappan Muthiah, Ravi Tandon, Naren Ramakrishnan

We propose hierarchical quickest change detection (HQCD), a framework that formalizes the process of incorporating additional correlated sources for early changepoint detection.

Change Detection Time Series +1

Flow of Information in Feed-Forward Deep Neural Networks

no code implementations20 Mar 2016 Pejman Khadivi, Ravi Tandon, Naren Ramakrishnan

Feed-forward deep neural networks have been used extensively in various machine learning applications.

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