Michael Albert
Postdoctoral Alumni
I'm motivated by practical problems at the intersection of learning, optimization, computation, and economics, specifically the design of markets (or mechanisms). As the world moves towards increasing automation, there are, more and more, opportunities to combine the wealth of data with a principled, provably optimal approach to market design in order to make intractable problems tractable and impossible problems possible.
Automated Design of Robust Mechanisms 2017
Michael Albert, Vincent Conitzer, and Peter Stone, In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), San Francisco, CA, USA, February 2017.
Mechanism Design with Unknown Correlated Distributions: Can We Learn Optimal Mechanisms? 2017
Michael Albert, Vincent Conitzer, and Peter Stone, In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems (AAMAS-17), Sau Paulo, Brazil, May 2017.
Real-time Adaptive Tolling Scheme for Optimized Social Welfare in Traffic Networks 2017
Guni Sharon, Josiah P. Hanna, Tarun Rambha, Michael W. Levin, Michael Albert, Stephen D. Boyles, and Peter Stone, In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2017), Sao Paulo, Brazil, May 2017.
Delta-Tolling: Adaptive Tolling for Optimizing Traffic Throughput 2016
Guni Sharon, Josiah Hanna, Tarun Rambha, Michael Albert, Peter Stone, and Stephen D. Boyles, In Proceedings of the 9th International Workshop on Agents in Traffic and Transportation (ATT 2016), New York, NY, USA, July 2016.
Minimum Cost Matching for Autonomous Carsharing 2016
Josiah P. Hanna, Michael Albert, Donna Chen, and Peter Stone, In Proceedings of the 9th IFAC Symposium on Intelligent Autonomous Vehicles (IAV 2016), Leipzig, Germany, June 2016.
Robust Automated Mechanism Design 2016
Michael Albert, Vincent Conitzer, and Peter Stone, In Proceedings of the EC 2016 2nd Algorithmic Game Theory and Data Science Workshop, Netherlands, July 2016.
Formerly affiliated with Learning Agents