Soham R. Phade

According to our database, Soham R. Phade authored at least 16 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Learning in Games with Cumulative Prospect Theoretic Preferences.

Soham R. Phade, Venkat Anantharam

Dyn. Games Appl., March, 2023

AI For Global Climate Cooperation 2023 Competition Proceedings.

Yoshua Bengio, Prateek Gupta, Lu Li, Soham Phade, Sunil Srinivasa, Andrew Williams, Tianyu Zhang, Yang Zhang, Stephan Zheng

CoRR, 2023

MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning.

Arundhati Banerjee, Soham Phade, Stefano Ermon, Stephan Zheng

CoRR, 2023

Online Pricing for Multi-User Multi-Item Markets.

Yigit Efe Erginbas, Thomas A. Courtade, Kannan Ramchandran, Soham Phade

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning Solutions in Large Economic Networks using Deep Multi-Agent Reinforcement Learning.

Michael Curry, Alexander Trott, Soham Phade, Yu Bai, Stephan Zheng

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

Interactive Learning with Pricing for Optimal and Stable Allocations in Markets.

Yigit Efe Erginbas, Soham Phade, Kannan Ramchandran

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N.

Tianyu Zhang, Andrew Williams, Soham Phade, Sunil Srinivasa, Yang Zhang, Prateek Gupta, Yoshua Bengio, Stephan Zheng

CoRR, 2022

Interactive Recommendations for Optimal Allocations in Markets with Constraints.

Yigit Efe Erginbas, Soham R. Phade, Kannan Ramchandran

CoRR, 2022

Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning.

Michael Curry, Alexander Trott, Soham Phade, Yu Bai, Stephan Zheng

CoRR, 2022

2021

Mechanism Design for Cumulative Prospect Theoretic Agents: A General Framework and the Revelation Principle.

Soham R. Phade, Venkat Anantharam

CoRR, 2021

2020

On the Impossibility of Convergence of Mixed Strategies with No Regret Learning.

Vidya Muthukumar, Soham Phade, Anant Sahai

CoRR, 2020

Utility-based Resource Allocation and Pricing for Serverless Computing.

Vipul Gupta, Soham R. Phade, Thomas A. Courtade, Kannan Ramchandran

CoRR, 2020

Black-Box Strategies and Equilibrium for Games with Cumulative Prospect Theoretic Players.

Soham R. Phade, Venkat Anantharam

CoRR, 2020

2019

On the Geometry of Nash and Correlated Equilibria with Cumulative Prospect Theoretic Preferences.

Soham R. Phade, Venkat Anantharam

Decis. Anal., 2019

Optimal Resource Allocation over Networks via Lottery-Based Mechanisms.

Soham R. Phade, Venkat Anantharam

Proceedings of the Game Theory for Networks, 2019

2017

A Distributed Boyle-Dykstra-Han Scheme.

Soham R. Phade, Vivek S. Borkar

SIAM J. Optim., 2017