Cobalt: Optimizing Mining Rewards in Proof-of-Work Network Games

no code implementations • 10 Jul 2023 • Arti Vedula, Abhishek Gupta, Shaileshh Bojja Venkatakrishnan

To maximize rewards a miner must choose its network connections carefully, ensuring existence of paths to other miners that are on average of a lower latency compared to paths between other miners.

PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks

no code implementations • 25 Jun 2023 • Saket Gurukar, Shaileshh Bojja Venkatakrishnan, Balaraman Ravindran, Srinivasan Parthasarathy

Specifically, the subgraph-based sampling approaches such as ClusterGCN and GraphSAINT have achieved state-of-the-art performance on the node classification tasks.

graph partitioning Node Classification +1

Kadabra: Adapting Kademlia for the Decentralized Web

1 code implementation • 23 Oct 2022 • Yunqi Zhang, Shaileshh Bojja Venkatakrishnan

A fundamental operation of applications in a decentralized Internet is data storage and retrieval.

Retrieval

Learning Generalizable Device Placement Algorithms for Distributed Machine Learning

1 code implementation • NeurIPS 2019 • Ravichandra Addanki, Shaileshh Bojja Venkatakrishnan, Shreyan Gupta, Hongzi Mao, Mohammad Alizadeh

We present Placeto, a reinforcement learning (RL) approach to efficiently find device placements for distributed neural network training.

BIG-bench Machine Learning Reinforcement Learning (RL)

Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning

3 code implementations • 20 Jun 2019 • Ravichandra Addanki, Shaileshh Bojja Venkatakrishnan, Shreyan Gupta, Hongzi Mao, Mohammad Alizadeh

Unlike prior approaches that only find a device placement for a specific computation graph, Placeto can learn generalizable device placement policies that can be applied to any graph.

BIG-bench Machine Learning Reinforcement Learning (RL)

Understanding & Generalizing AlphaGo Zero

no code implementations • ICLR 2019 • Ravichandra Addanki, Mohammad Alizadeh, Shaileshh Bojja Venkatakrishnan, Devavrat Shah, Qiaomin Xie, Zhi Xu

AlphaGo Zero (AGZ) introduced a new {\em tabula rasa} reinforcement learning algorithm that has achieved superhuman performance in the games of Go, Chess, and Shogi with no prior knowledge other than the rules of the game.

Decision Making reinforcement-learning +2

Learning Scheduling Algorithms for Data Processing Clusters

2 code implementations • 3 Oct 2018 • Hongzi Mao, Malte Schwarzkopf, Shaileshh Bojja Venkatakrishnan, Zili Meng, Mohammad Alizadeh

Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms.

Reinforcement Learning (RL) Scheduling

Variance Reduction for Reinforcement Learning in Input-Driven Environments

no code implementations • ICLR 2019 • Hongzi Mao, Shaileshh Bojja Venkatakrishnan, Malte Schwarzkopf, Mohammad Alizadeh

We consider reinforcement learning in input-driven environments, where an exogenous, stochastic input process affects the dynamics of the system.

Meta-Learning Object Tracking +3

Graph2Seq: Scalable Learning Dynamics for Graphs

no code implementations • ICLR 2018 • Shaileshh Bojja Venkatakrishnan, Mohammad Alizadeh, Pramod Viswanath

By not limiting the representation to a fixed dimension, Graph2Seq scales naturally to graphs of arbitrary sizes and shapes.

Combinatorial Optimization Time Series +1

Dandelion: Redesigning the Bitcoin Network for Anonymity

2 code implementations • 16 Jan 2017 • Shaileshh Bojja Venkatakrishnan, Giulia Fanti, Pramod Viswanath

We propose a simple networking policy called Dandelion, which achieves nearly-optimal anonymity guarantees at minimal cost to the network's utility.

Cryptography and Security Information Theory Information Theory