Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces
暂无分享,去创建一个
Andreas Krause | Johannes Kirschner | Rasmus Ischebeck | Mojmir Mutny | Nicole Hiller | Andreas Krause | R. Ischebeck | N. Hiller | Johannes Kirschner | Mojmír Mutný
[1] Andreas Krause,et al. Information Directed Sampling and Bandits with Heteroscedastic Noise , 2018, COLT.
[2] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[3] Aditya Gopalan,et al. On Kernelized Multi-armed Bandits , 2017, ICML.
[4] Joel W. Burdick,et al. Stagewise Safe Bayesian Optimization with Gaussian Processes , 2018, ICML.
[5] Ameet Talwalkar,et al. Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization , 2016, J. Mach. Learn. Res..
[6] Andreas Krause,et al. Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics , 2016, Machine Learning.
[7] Volkan Cevher,et al. High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups , 2018, AISTATS.
[8] Dino Sejdinovic,et al. Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences , 2018, ArXiv.
[9] Donald R. Jones,et al. Direct Global Optimization Algorithm , 2009, Encyclopedia of Optimization.
[10] Andreas Krause,et al. High-Dimensional Gaussian Process Bandits , 2013, NIPS.
[11] Volkan Cevher,et al. Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization , 2017, COLT.
[12] Alkis Gotovos,et al. Safe Exploration for Optimization with Gaussian Processes , 2015, ICML.
[13] Andreas Krause,et al. Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting , 2009, IEEE Transactions on Information Theory.
[14] Nando de Freitas,et al. Taking the Human Out of the Loop: A Review of Bayesian Optimization , 2016, Proceedings of the IEEE.
[15] A Stochastic Line Search Method with Convergence Rate Analysis , 2018, 1807.07994.
[16] Csaba Szepesvari,et al. Online learning for linearly parametrized control problems , 2012 .
[17] F. Kozin,et al. System Modeling and Optimization , 1982 .
[18] Jonathan Scarlett,et al. Tight Regret Bounds for Bayesian Optimization in One Dimension , 2018, ICML.
[19] Yurii Nesterov,et al. Efficiency of Coordinate Descent Methods on Huge-Scale Optimization Problems , 2012, SIAM J. Optim..
[20] M. J. D. Powell,et al. On search directions for minimization algorithms , 1973, Math. Program..
[21] Philipp Hennig,et al. Probabilistic Line Searches for Stochastic Optimization , 2015, NIPS.
[22] Andreas Christmann,et al. Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.
[23] Stefano Ermon,et al. Sparse Gaussian Processes for Bayesian Optimization , 2016, UAI.
[24] Yang Yu,et al. Derivative-Free Optimization of High-Dimensional Non-Convex Functions by Sequential Random Embeddings , 2016, IJCAI.
[25] J. Mockus,et al. The Bayesian approach to global optimization , 1989 .
[26] Yoshua Bengio,et al. Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..
[27] Shalabh Bhatnagar,et al. Stochastic Recursive Algorithms for Optimization , 2012 .
[28] Nikolaus Hansen,et al. Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.
[29] Stephen J. Roberts,et al. Optimization, fast and slow: optimally switching between local and Bayesian optimization , 2018, ICML.
[30] Carsten Rockstuhl,et al. Benchmarking Five Global Optimization Approaches for Nano-optical Shape Optimization and Parameter Reconstruction , 2018, ACS Photonics.
[31] Zi Wang,et al. Max-value Entropy Search for Efficient Bayesian Optimization , 2017, ICML.
[32] Stefano Ermon,et al. Bayesian Optimization of FEL Performance at LCLS , 2016 .
[33] J. M. Martínez,et al. A derivative-free nonmonotone line-search technique for unconstrained optimization , 2008 .
[34] Petros Koumoutsakos,et al. Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) , 2003, Evolutionary Computation.
[35] Ohad Shamir,et al. On the Complexity of Bandit and Derivative-Free Stochastic Convex Optimization , 2012, COLT.
[36] Nando de Freitas,et al. Bayesian Optimization in a Billion Dimensions via Random Embeddings , 2013, J. Artif. Intell. Res..
[37] Sham M. Kakade,et al. Information Consistency of Nonparametric Gaussian Process Methods , 2008, IEEE Transactions on Information Theory.
[38] Serge Gratton,et al. Direct Search Based on Probabilistic Descent , 2015, SIAM J. Optim..
[39] Katya Scheinberg,et al. Global convergence rate analysis of unconstrained optimization methods based on probabilistic models , 2015, Mathematical Programming.
[40] Cheng Li,et al. High Dimensional Bayesian Optimization using Dropout , 2018, IJCAI.
[41] Yurii Nesterov,et al. Random Gradient-Free Minimization of Convex Functions , 2015, Foundations of Computational Mathematics.
[42] Adam Tauman Kalai,et al. Online convex optimization in the bandit setting: gradient descent without a gradient , 2004, SODA '05.
[43] Andreas Krause,et al. Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features , 2018, NeurIPS.
[44] Andreas Krause,et al. Safe learning of regions of attraction for uncertain, nonlinear systems with Gaussian processes , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).