1 citations · 1 across the 2 of their papers we have counts for
5 papers
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…
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…