42 citations · 44 across the 8 of their papers we have counts for
10 papers
Atlas Urban Index: A VLM-Based Approach for Spatially and Temporally Calibrated Urban Development Monitoring
Mithul Chander, Sai Pragnya Ranga, Prathamesh Mayekar
We introduce the {\em Atlas Urban Index} (AUI), a metric for measuring urban development computed using Sentinel-2 \citep{spoto2012sentinel2} satellite imagery. Existing approaches…
Fundamental Limits of Distributed Optimization over Multiple Access Channel
Shubham Jha
We consider distributed optimization over a -dimensional space, where remote clients send coded gradient estimates over an {\em additive Gaussian Multiple Access Channel (MA…
Communication-Constrained Bandits under Additive Gaussian Noise
Prathamesh Mayekar, Jonathan Scarlett, Vincent Y. F. Tan
We study a distributed stochastic multi-armed bandit where a client supplies the learner with communication-constrained feedback based on the rewards for the corresponding arm pull…
Compression for Distributed Optimization and Timely Updates
Prathamesh Mayekar
The goal of this thesis is to study the compression problems arising in distributed computing systematically. In the first part of the thesis, we study gradient compression for dis…
Fundamental limits of over-the-air optimization: Are analog schemes optimal?
Shubham K Jha, Prathamesh Mayekar, Himanshu Tyagi
We consider over-the-air convex optimization on a dimensional space where coded gradients are sent over an additive Gaussian noise channel with variance . The codewords sa…
Information-constrained optimization: can adaptive processing of gradients help?
Jayadev Acharya, Clément L. Canonne, Prathamesh Mayekar +1
We revisit first-order optimization under local information constraints such as local privacy, gradient quantization, and computational constraints limiting access to a few coordin…
Wyner-Ziv Estimators for Distributed Mean Estimation with Side Information and Optimization
Prathamesh Mayekar, Shubham Jha, Ananda Theertha Suresh +1
Communication efficient distributed mean estimation is an important primitive that arises in many distributed learning and optimization scenarios such as federated learning. Withou…
Limits on Gradient Compression for Stochastic Optimization
Prathamesh Mayekar, Himanshu Tyagi
We consider stochastic optimization over spaces using access to a first-order oracle. We ask: {What is the minimum precision required for oracle outputs to retain the unre…
RATQ: A Universal Fixed-Length Quantizer for Stochastic Optimization
Prathamesh Mayekar, Himanshu Tyagi
We present Rotated Adaptive Tetra-iterated Quantizer (RATQ), a fixed-length quantizer for gradients in first order stochastic optimization. RATQ is easy to implement and involves o…
Optimal Source Codes for Timely Updates
Prathamesh Mayekar, Parimal Parag, Himanshu Tyagi
A transmitter observing a sequence of independent and identically distributed random variables seeks to keep a receiver updated about its latest observations. The receiver need not…