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The Physical Systems Behind Optimization Algorithms
Lin Yang, Raman Arora, Vladimir braverman, Tuo Zhao
Neural Information Processing Systems http://nips.cc/
No-Press Diplomacy: Modeling Multi-Agent Gameplay
Philip Paquette, Yuchen Lu, SETON STEVEN BOCCO, Max Smith, Satya O.-G., Jonathan K. Kummerfeld, Joelle Pineau, Satinder Singh, Aaron C. Courville
Neural Information Processing Systems http://nips.cc/
EfficientHighDimensionalBayesianOptimization withAdditivityandQuadratureFourierFeatures
Bayesian Optimization (BO) is a versatile method for global optimization of a black-box function using noisy point-wise observations.
MetaGAN: An Adversarial Approach to Few-Shot Learning
Ruixiang ZHANG, Tong Che, Zoubin Ghahramani, Yoshua Bengio, Yangqiu Song
Neural Information Processing Systems http://nips.cc/
Shadowing Properties of Optimization Algorithms
Antonio Orvieto, Aurelien Lucchi
Analyzing the convergence properties of these algorithms can be complex, especially for NAG whose convergence proof relies on algebraic tricks that reveal little detail about the acceleration phenomenon, i.e. the celebrated optimality of NAG in convex smooth optimization. Instead, an alternative approach is to view these methods as numerical integrators of some ordinary differential equations (ODEs).
