activity
20182026
most citedPlaceto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning

30 citations · 38 across the 4 of their papers we have counts for

collaborators

11 papers

cs.CR2026

Rethinking Collaborative Trust for Verifiably Decentralized Blockchain Systems

Yunqi Zhang, Shaileshh Bojja Venkatakrishnan

Despite the promise of decentralization, measurement studies have identified a conspicuous lack of decentralization in blockchains. Centralization has been observed in almost all l…

cs.NI2026

Starfield: Demand-Aware Satellite Topology Design for Low-Earth Orbit Mega Constellations

Shayan Hamidi Dehshali, Tzu-Hsuan Liao, Shaileshh Bojja Venkatakrishnan

Low-Earth orbit (LEO) mega-constellations are emerging as high-capacity backbones for next-generation Internet. Deployment of laser terminals enables high-bandwidth, low-latency in…

cs.NI20223 cited

Less is More: Fairness in Wide-Area Proof-of-Work Blockchain Networks

Yifan Mao, Shaileshh Bojja Venkatakrishnan

Blockchain is rapidly emerging as an important class of network application, with a unique set of trust, security and transparency properties. In a blockchain system, participants…

cs.SI2021

The Effect of Network Topology on Credit Network Throughput

Vibhaalakshmi Sivaraman, Weizhao Tang, Shaileshh Bojja Venkatakrishnan +2

Credit networks rely on decentralized, pairwise trust relationships (channels) to exchange money or goods. Credit networks arise naturally in many financial systems, including the…

cs.NI20205 cited

Perigee: Efficient Peer-to-Peer Network Design for Blockchains

Yifan Mao, Soubhik Deb, Shaileshh Bojja Venkatakrishnan +2

A key performance metric in blockchains is the latency between when a transaction is broadcast and when it is confirmed (the so-called, confirmation latency). While improvements in…

cs.LG201930 cited

Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning

Ravichandra Addanki, Shaileshh Bojja Venkatakrishnan, Shreyan Gupta +2

We present Placeto, a reinforcement learning (RL) approach to efficiently find device placements for distributed neural network training. Unlike prior approaches that only find a d…