| Utility-based resource allocation and pricing for serverless computing V Gupta, S Phade, T Courtade, K Ramchandran arXiv preprint arXiv:2008.07793, 2020 | 12 | 2020 |
| On the geometry of Nash and correlated equilibria with cumulative prospect theoretic preferences SR Phade, V Anantharam Decision Analysis 16 (2), 142-156, 2019 | 10 | 2019 |
| A Distributed Boyle--Dykstra--Han Scheme SR Phade, VS Borkar SIAM Journal on Optimization 27 (3), 1880-1897, 2017 | 10 | 2017 |
| Learning in games with cumulative prospect theoretic preferences SR Phade, V Anantharam Dynamic Games and Applications, 1-42, 2023 | 6 | 2023 |
| AI for global climate cooperation: modeling global climate negotiations, agreements, and long-term cooperation in RICE-N T Zhang, A Williams, S Phade, S Srinivasa, Y Zhang, P Gupta, Y Bengio, ... arXiv preprint arXiv:2208.07004, 2022 | 6 | 2022 |
| Finding general equilibria in many-agent economic simulations using deep reinforcement learning M Curry, AR Trott, S Phade, Y Bai, S Zheng | 6 | 2021 |
| Optimal resource allocation over networks via lottery-based mechanisms S Phade, V Anantharam International Conference on Game Theory for Networks, 51-70, 2019 | 4 | 2019 |
| Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning M Curry, A Trott, S Phade, Y Bai, S Zheng arXiv preprint arXiv:2201.01163, 2022 | 2 | 2022 |
| Black-Box Strategies and Equilibrium for Games with Cumulative Prospect Theoretic Players SR Phade, V Anantharam arXiv preprint arXiv:2004.09592, 2020 | 2 | 2020 |
| Learning Solutions in Large Economic Networks using Deep Multi-Agent Reinforcement Learning. M Curry, A Trott, S Phade, Y Bai, S Zheng AAMAS, 2760-2762, 2023 | 1 | 2023 |
| Interactive Recommendations for Optimal Allocations in Markets with Constraints YE Erginbas, S Phade, K Ramchandran arXiv preprint arXiv:2207.04143, 2022 | 1 | 2022 |
| Mechanism Design for Cumulative Prospect Theoretic Agents: A General Framework and the Revelation Principle SR Phade, V Anantharam arXiv preprint arXiv:2101.08722, 2021 | 1 | 2021 |
| On the Impossibility of Convergence of Mixed Strategies with No Regret Learning V Muthukumar, S Phade, A Sahai arXiv preprint arXiv:2012.02125, 2020 | 1 | 2020 |
| Systems and methods for model-based meta-learning A Banerjee, S Zheng, S Phade, S Ermon US Patent App. 18/159,036, 2024 | | 2024 |
| Online Pricing for Multi-User Multi-Item Markets YE Erginbas, T Courtade, K Ramchandran, S Phade Advances in Neural Information Processing Systems 36, 29718-29740, 2023 | | 2023 |
| AI For Global Climate Cooperation 2023 Competition Proceedings Y Bengio, P Gupta, L Li, S Phade, S Srinivasa, A Williams, T Zhang, ... arXiv preprint arXiv:2307.06951, 2023 | | 2023 |
| Interactive Learning with Pricing for Optimal and Stable Allocations in Markets YE Erginbas, S Phade, K Ramchandran International Conference on Artificial Intelligence and Statistics, 9773-9806, 2023 | | 2023 |
| MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning A Banerjee, S Phade, S Ermon, S Zheng arXiv preprint arXiv:2304.04668, 2023 | | 2023 |
| MERMADE: -shot Robust Adaptive Mechanism Design via Model-Based Meta-Learning A Banerjee, SR Phade, S Ermon, S Zheng | | 2022 |
| Behavioral Network Economics SR Phade University of California, Berkeley, 2021 | | 2021 |
Kannan RamchandranProfessor of Electrical Engineering and Computer Science, UC BerkeleyVerified email at eecs.berkeley.edu
