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OptimizingConditionalValue-At-Risk ofBlack-BoxFunctions
Neural Information Processing Systems
A wide range of applications from Auto-ML [15] to chemistry [6] and drug design [3] require optimizing ablack-boxobjectivefunction (i.e.,itsclosed-form expression, gradient, andconvexity are unknown) through observing noisy function evaluations.
Technology: Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (0.47)
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- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.69)
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- North America > United States > California > Los Angeles County > Los Angeles (0.05)
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Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)
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Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.96)
DeepSynopticMonteCarloPlanningin ReconnaissanceBlindChess
Neural Information Processing Systems
This paper introduces deep synoptic Monte Carlo planning (DSMCP) for large imperfect information games.
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- North America > United States > Massachusetts > Middlesex County > Cambridge (0.04)
- Asia > Middle East > Jordan (0.04)