research
∙
06/13/2023
Implicit Compressibility of Overparametrized Neural Networks Trained with Heavy-Tailed SGD
Neural network compression has been an increasingly important subject, d...
share
research
∙
06/09/2023
Federated Learning You May Communicate Less Often!
We investigate the generalization error of statistical learning models i...
research
∙
04/24/2023
More Communication Does Not Result in Smaller Generalization Error in Federated Learning
We study the generalization error of statistical learning models in a Fe...
research
∙
03/09/2023
Data-dependent Generalization Bounds via Variable-Size Compressibility
In this paper, we establish novel data-dependent upper bounds on the gen...
research
∙
06/06/2022
Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning
In this paper, we use tools from rate-distortion theory to establish new...
research
∙
07/12/2021
Rate-Exponent Region for a Class of Distributed Hypothesis Testing Against Conditional Independence Problems
We study a class of K-encoder hypothesis testing against conditional ind...
research
∙
07/07/2021
In-Network Learning: Distributed Training and Inference in Networks
It is widely perceived that leveraging the success of modern machine lea...
research
∙
05/25/2021
On learning parametric distributions from quantized samples
We consider the problem of learning parametric distributions from their ...
research
∙
04/30/2021
On In-network learning. A Comparative Study with Federated and Split Learning
In this paper, we consider a problem in which distributively extracted f...
research
∙
02/15/2021
Scalable Vector Gaussian Information Bottleneck
In the context of statistical learning, the Information Bottleneck metho...
research
∙
11/02/2020
On the Relevance-Complexity Region of Scalable Information Bottleneck
The Information Bottleneck method is a learning technique that seeks a r...
research
∙
05/14/2020
Some Results on the Vector Gaussian Hypothesis Testing Problem
This paper studies the problem of discriminating two multivariate Gaussi...
research
∙
01/31/2020
On the Information Bottleneck Problems: Models, Connections, Applications and Information Theoretic Views
This tutorial paper focuses on the variants of the bottleneck problem ta...
research
∙
07/18/2019
Distributed Hypothesis Testing: Cooperation and Concurrent Detection
A single-sensor two-detectors system is considered where the sensor comm...
research
∙
05/28/2019
Variational Information Bottleneck for Unsupervised Clustering: Deep Gaussian Mixture Embedding
In this paper, we develop an unsupervised generative clustering framewor...
research
∙
04/04/2019
Optimal Rate-Exponent Region for a Class of Hypothesis Testing Against Conditional Independence Problems
We study a class of distributed hypothesis testing against conditional i...
research
∙
02/25/2019
Vector Gaussian CEO Problem Under Logarithmic Loss
In this paper, we study the vector Gaussian Chief Executive Officer (CEO...
research
∙
11/09/2018
Vector Gaussian CEO Problem Under Logarithmic Loss and Applications
We study the vector Gaussian CEO problem under logarithmic loss distorti...
research
∙
10/08/2018
Distributed Hypothesis Testing with Collaborative Detection
A detection system with a single sensor and two detectors is considered,...
research
∙
07/11/2018
Distributed Variational Representation Learning
The problem of distributed representation learning is one in which multi...
research
∙
05/16/2018