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Learning with proxy supervision for end-to-end visual learning.

, and . Intelligent Vehicles Symposium, page 1-6. IEEE, (2017)

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Automated Construction of Bounded-Loss Imperfect-Recall Abstractions in Extensive-Form Games (Extended Abstract)., , and . IJCAI, page 5030-5034. ijcai.org, (2020)Journal track.Combining Incremental Strategy Generation and Branch and Bound Search for Computing Maxmin Strategies in Imperfect Recall Games., , and . AAMAS, page 902-910. ACM, (2017)Towards Solving Imperfect Recall Games., and . AAAI Workshops, volume WS-17 of AAAI Technical Report, AAAI Press, (2017)Algorithms for computing strategies in two-player simultaneous move games., , , , and . Artif. Intell., (2016)Solving Imperfect Recall Games.. AAMAS, page 1820-1821. ACM, (2017)Learning with proxy supervision for end-to-end visual learning., and . Intelligent Vehicles Symposium, page 1-6. IEEE, (2017)An Algorithm for Constructing and Solving Imperfect Recall Abstractions of Large Extensive-Form Games., , and . IJCAI, page 936-942. ijcai.org, (2017)Double-oracle algorithm for computing an exact nash equilibrium in zero-sum extensive-form games., , , , and . AAMAS, page 335-342. IFAAMAS, (2013)Computing Maxmin Strategies in Extensive-form Zero-sum Games with Imperfect Recall., , , and . ICAART (2), page 63-74. SciTePress, (2017)Strategy Effectiveness of Game-Theoretical Solution Concepts in Extensive-Form General-Sum Games., , and . AAMAS, page 1813-1814. ACM, (2015)