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Sunil Srinivasa
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2020 – today
- 2023
- [i7]Yoshua Bengio, Prateek Gupta, Lu Li, Soham Phade, Sunil Srinivasa, Andrew Williams, Tianyu Zhang, Yang Zhang, Stephan Zheng:
AI For Global Climate Cooperation 2023 Competition Proceedings. CoRR abs/2307.06951 (2023) - 2022
- [j6]Tian Lan, Sunil Srinivasa, Huan Wang, Stephan Zheng:
WarpDrive: Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU. J. Mach. Learn. Res. 23: 316:1-316:6 (2022) - [i6]Tianyu Zhang, Andrew Williams, Soham Phade, Sunil Srinivasa, Yang Zhang, Prateek Gupta, Yoshua Bengio, Stephan Zheng:
AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N. CoRR abs/2208.07004 (2022) - 2021
- [i5]Stephan Zheng, Alexander Trott, Sunil Srinivasa, David C. Parkes, Richard Socher:
The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning. CoRR abs/2108.02755 (2021) - [i4]Alexander Trott, Sunil Srinivasa, Douwe van der Wal, Sebastien Haneuse, Stephan Zheng:
Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist. CoRR abs/2108.02904 (2021) - [i3]Tian Lan, Sunil Srinivasa, Stephan Zheng:
WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU. CoRR abs/2108.13976 (2021) - 2020
- [i2]Stephan Zheng, Alexander Trott, Sunil Srinivasa, Nikhil Naik, Melvin Gruesbeck, David C. Parkes, Richard Socher:
The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies. CoRR abs/2004.13332 (2020)
2010 – 2019
- 2018
- [c7]Sunil Srinivasa, Girish Kathalagiri, Julu Subramanyam Varanasi, Luis Carlos Quintela, Mohamad Charafeddine, Chi-Hoon Lee:
On Optimizing Operational Efficiency in Storage Systems via Deep Reinforcement Learning. ECML/PKDD (3) 2018: 238-253 - 2017
- [i1]Nithyanand Kota, Abhishek Mishra, Sunil Srinivasa, Xi Chen, Pieter Abbeel:
A K-fold Method for Baseline Estimation in Policy Gradient Algorithms. CoRR abs/1701.00867 (2017) - 2014
- [j5]Sunil Srinivasa, Martin Haenggi
:
Combining stochastic geometry and statistical mechanics for the analysis and design of mesh networks. Ad Hoc Networks 13: 110-122 (2014) - 2012
- [j4]Sunil Srinivasa, Martin Haenggi
A Statistical Mechanics-Based Framework to Analyze Ad Hoc Networks with Random Access. IEEE Trans. Mob. Comput. 11(4): 618-630 (2012) - [j3]Sundaram Vanka, Sunil Srinivasa, Zhenhua Gong, Peter Vizi, Kostas Stamatiou, Martin Haenggi
Superposition Coding Strategies: Design and Experimental Evaluation. IEEE Trans. Wirel. Commun. 11(7): 2628-2639 (2012) - [c6]Sundaram Vanka, Sunil Srinivasa, Martin Haenggi
A practical approach to strengthen vulnerable downlinks using superposition coding. ICC 2012: 3763-3768 - 2010
- [j2]Sunil Srinivasa, Martin Haenggi
Distance Distributions in Finite Uniformly Random Networks: Theory and Applications. IEEE Trans. Veh. Technol. 59(2): 940-949 (2010) - [c5]Sunil Srinivasa, Martin Haenggi
Throughput-delay-reliability tradeoffs in multihop networks with random access. Allerton 2010: 1117-1124 - [c4]Radha Krishna Ganti
Implementation and Experimental Results of Superposition Coding on Software Radio. ICC 2010: 1-5 - [c3]Sunil Srinivasa, Martin Haenggi
The TASEP: A Statistical Mechanics Tool to Study the Performance of Wireless Line Networks. ICCCN 2010: 1-6
2000 – 2009
- 2009
- [c2]Sunil Srinivasa, Sudha Krishnamurthy:
CREST: An Opportunistic Forwarding Protocol Based on Conditional Residual Time. SECON 2009: 1-9 - 2007
- [j1]Sunil Srinivasa, Syed Ali Jafar
The Optimality of Transmit Beamforming: A Unified View. IEEE Trans. Inf. Theory 53(4): 1558-1564 (2007) - 2006
- [c1]Sunil Srinivasa, Martin Haenggi
Simplified Analysis and Design of MIMO Ad Hoc Networks. GLOBECOM 2006
Coauthor Index
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