A framework for optimization under limited information
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[1] TANSU ALPCAN,et al. A Risk-Based Approach to Optimisation under Limited Information , 2012 .
[2] Christopher M. Bishop,et al. Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .
[3] Thomas Bäck,et al. Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .
[4] J. F. Price,et al. On descent from local minima , 1971 .
[5] Iain Murray. Introduction To Gaussian Processes , 2008 .
[6] Jasbir S. Arora,et al. Survey of multi-objective optimization methods for engineering , 2004 .
[7] Tansu Alpcan,et al. A system performance approach to OSNR optimization in optical networks , 2010, IEEE Transactions on Communications.
[8] David H. Wolpert,et al. No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..
[9] Kaare Brandt Petersen,et al. The Matrix Cookbook , 2006 .
[10] Oleksandr Romanko,et al. Normalization and Other Topics in MultiObjective Optimization , 2006 .
[11] M Rinehart,et al. The value of sequential information in shortest path optimization , 2010, Proceedings of the 2010 American Control Conference.
[12] Anthony Widjaja,et al. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.
[13] Nikolaos V. Sahinidis,et al. Optimization under uncertainty: state-of-the-art and opportunities , 2004, Comput. Chem. Eng..
[14] Oleksandr Romanko,et al. Discussions on Normalization and Other Topics in Multi-Objective Optimization Algorithmics Group, Fields Industrial Problem Solving Workshop , 2006 .
[15] Art Lew,et al. Dynamic Programming: an overview , 2006 .
[16] Burr Settles,et al. Active Learning Literature Survey , 2009 .
[17] Phillip Boyle,et al. Gaussian Processes for Regression and Optimisation , 2007 .
[18] Keith R. Thompson,et al. Implementation of gaussian process models for non-linear system identification , 2009 .
[19] R. Tempo,et al. Randomized Algorithms for Analysis and Control of Uncertain Systems , 2004 .
[20] D. Ackley. A connectionist machine for genetic hillclimbing , 1987 .
[21] N. Zheng,et al. Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models , 2006, J. Glob. Optim..
[22] R. Bellman. Dynamic programming. , 1957, Science.
[23] B. John Oommen,et al. On the optimal search problem: the case when the target distribution is unknown , 1997, Proceedings 17th International Conference of the Chilean Computer Science Society.
[24] Michael E. Tipping. Bayesian Inference: An Introduction to Principles and Practice in Machine Learning , 2003, Advanced Lectures on Machine Learning.
[25] J. Pierce,et al. A New Look at the Relation between Information Theory and Search Theory , 1978 .
[26] Riccardo Poli,et al. Particle swarm optimization , 1995, Swarm Intelligence.
[27] Klaus Obermayer,et al. Gaussian process regression: active data selection and test point rejection , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.
[28] Derong Liu. The Mathematics of Internet Congestion Control , 2005, IEEE Transactions on Automatic Control.
[29] M. Sniedovich,et al. A new look at Bellman's principle of optimality , 1986 .
[30] David J. C. MacKay,et al. Information-Based Objective Functions for Active Data Selection , 1992, Neural Computation.
[31] Donald R. Jones,et al. A Taxonomy of Global Optimization Methods Based on Response Surfaces , 2001, J. Glob. Optim..
[32] Christopher K. I. Williams,et al. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) , 2005 .
[33] F. H. Branin. Widely convergent method for finding multiple solutions of simultaneous nonlinear equations , 1972 .
[34] Paul M. B. Vitányi,et al. An Introduction to Kolmogorov Complexity and Its Applications, Third Edition , 1997, Texts in Computer Science.
[35] Anne Auger,et al. When Do Heavy-Tail Distributions Help? , 2006, PPSN.
[36] Rob A. Rutenbar,et al. Simulated annealing algorithms: an overview , 1989, IEEE Circuits and Devices Magazine.
[37] Rayadurgam Srikant,et al. The Mathematics of Internet Congestion Control , 2003 .
[38] R. Tempo,et al. Probabilistic robustness analysis: explicit bounds for the minimum number of samples , 1996, Proceedings of 35th IEEE Conference on Decision and Control.
[39] Tansu Alpcan,et al. Power control for multicell CDMA wireless networks: A team optimization approach , 2005, Third International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt'05).
[40] David J. C. MacKay,et al. Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.
[41] Thomas Bäck,et al. Evolutionary Algorithms in Theory and Practice , 1996 .
[42] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[43] M. Sniedovich. Dijkstra's algorithm revisited: the dynamic programming connexion , 2006 .
[44] E. Jaynes. Entropy and Search Theory , 1985 .
[45] Carl E. Rasmussen,et al. State-Space Inference and Learning with Gaussian Processes , 2010, AISTATS.
[46] Ming Li,et al. An Introduction to Kolmogorov Complexity and Its Applications , 2019, Texts in Computer Science.
[47] Rayadurgam Srikant,et al. The Mathematics of Internet Congestion Control (Systems and Control: Foundations and Applications) , 2004 .
[48] Neil D. Lawrence,et al. Fast Sparse Gaussian Process Methods: The Informative Vector Machine , 2002, NIPS.
[49] D. V. Gokhale,et al. Entropy expressions and their estimators for multivariate distributions , 1989, IEEE Trans. Inf. Theory.