184 citations · 779 across the 55 of their papers we have counts for
71 papers
Optimal Compression of Unit Norm Vectors in the High Distortion Regime
Heng Zhu, Avishek Ghosh, Arya Mazumdar
Motivated by the need for communication-efficient distributed learning, we investigate the method for compressing a unit norm vector into the minimum number of bits, while still al…
On the sample complexity of parameter estimation in logistic regression with normal design
Daniel Hsu, Arya Mazumdar
The logistic regression model is one of the most popular data generation model in noisy binary classification problems. In this work, we study the sample complexity of estimating t…
Optimization using Parallel Gradient Evaluations on Multiple Parameters
Yash Chandak, Shiv Shankar, Venkata Gandikota +2
We propose a first-order method for convex optimization, where instead of being restricted to the gradient from a single parameter, gradients from multiple parameters can be used d…
Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks
Xiaofan Yu, Ludmila Cherkasova, Harsh Vardhan +5
Federated Learning (FL) has gained increasing interest in recent years as a distributed on-device learning paradigm. However, multiple challenges remain to be addressed for deployi…
An Improved Algorithm for Clustered Federated Learning
Harshvardhan, Avishek Ghosh, Arya Mazumdar
In this paper, we address the dichotomy between heterogeneous models and simultaneous training in Federated Learning (FL) via a clustering framework. We define a new clustering mod…
Binary Iterative Hard Thresholding Converges with Optimal Number of Measurements for 1-Bit Compressed Sensing
Namiko Matsumoto, Arya Mazumdar
Compressed sensing has been a very successful high-dimensional signal acquisition and recovery technique that relies on linear operations. However, the actual measurements of signa…