no code implementations • 10 Jul 2023 • Yoshua Bengio, Prateek Gupta, Lu Li, Soham Phade, Sunil Srinivasa, Andrew Williams, Tianyu Zhang, Yang Zhang, Stephan Zheng
On the other hand, an interdisciplinary panel of human experts in law, policy, sociology, economics and environmental science, evaluated the solutions qualitatively.
no code implementations • 10 Apr 2023 • Arundhati Banerjee, Soham Phade, Stefano Ermon, Stephan Zheng
We then show that our model-based meta-learning approach is cost-effective in intervening on bandit agents with unseen explore-exploit strategies.
no code implementations • 13 Dec 2022 • Yigit Efe Erginbas, Soham Phade, Kannan Ramchandran
Large-scale online recommendation systems must facilitate the allocation of a limited number of items among competing users while learning their preferences from user feedback.
2 code implementations • 15 Aug 2022 • Tianyu Zhang, Andrew Williams, Soham Phade, Sunil Srinivasa, Yang Zhang, Prateek Gupta, Yoshua Bengio, Stephan Zheng
To facilitate this research, here we introduce RICE-N, a multi-region integrated assessment model that simulates the global climate and economy, and which can be used to design and evaluate the strategic outcomes for different negotiation and agreement frameworks.
no code implementations • 8 Jul 2022 • Yigit Efe Erginbas, Soham Phade, Kannan Ramchandran
Recommendation systems when employed in markets play a dual role: they assist users in selecting their most desired items from a large pool and they help in allocating a limited number of items to the users who desire them the most.
no code implementations • 3 Jan 2022 • Michael Curry, Alexander Trott, Soham Phade, Yu Bai, Stephan Zheng
We validate the learned solutions are $\epsilon$-meta-equilibria through best-response analyses, show that they align with economic intuitions, and show our approach can learn a spectrum of qualitatively distinct $\epsilon$-meta-equilibria in open RBC models.
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
+2
no code implementations • 3 Dec 2020 • Vidya Muthukumar, Soham Phade, Anant Sahai
We study the limiting behavior of the mixed strategies that result from optimal no-regret learning strategies in a repeated game setting where the stage game is any 2 by 2 competitive game.
1 code implementation • 18 Aug 2020 • Vipul Gupta, Soham Phade, Thomas Courtade, Kannan Ramchandran
As one of the fastest-growing cloud services, serverless computing provides an opportunity to better serve both users and providers through the incorporation of market-based strategies for pricing and resource allocation.
Distributed, Parallel, and Cluster Computing Computer Science and Game Theory