Learning dynamic algorithm portfolios
暂无分享,去创建一个
[1] John Taylor Stallings,et al. The Search For Satisfaction , 1935 .
[2] E. Kaplan,et al. Nonparametric Estimation from Incomplete Observations , 1958 .
[3] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[4] Wayne Nelson,et al. Applied life data analysis , 1983 .
[5] Nichael Lynn Cramer,et al. A Representation for the Adaptive Generation of Simple Sequential Programs , 1985, ICGA.
[6] R. Geoff Dromey,et al. An algorithm for the selection problem , 1986, Softw. Pract. Exp..
[7] P. W. Jones,et al. Bandit Problems, Sequential Allocation of Experiments , 1987 .
[8] Oren Etzioni,et al. Embedding Decision-Analytic Control in a Learning Architecture , 1991, Artif. Intell..
[9] Stuart J. Russell,et al. Principles of Metareasoning , 1989, Artif. Intell..
[10] Hector J. Levesque,et al. Hard and Easy Distributions of SAT Problems , 1992, AAAI.
[11] David Zuckerman,et al. Optimal Speedup of Las Vegas Algorithms , 1993, Inf. Process. Lett..
[12] Shlomo Zilberstein,et al. Anytime Sensing Planning and Action: A Practical Model for Robot Control , 1993, IJCAI.
[13] Mark S. Boddy,et al. Deliberation Scheduling for Problem Solving in Time-Constrained Environments , 1994, Artif. Intell..
[14] Andrew W. Moore,et al. Efficient Algorithms for Minimizing Cross Validation Error , 1994, ICML.
[15] Nicolò Cesa-Bianchi,et al. Gambling in a rigged casino: The adversarial multi-armed bandit problem , 1995, Proceedings of IEEE 36th Annual Foundations of Computer Science.
[16] Hani Doss,et al. An Approach to Nonparametric Regression for Life History Data Using Local Linear Fitting , 1995 .
[17] O. Linton,et al. Kernel estimation in a nonparametric marker dependent hazard model , 1995 .
[18] Andrew W. Moore,et al. Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..
[19] Edward P. K. Tsang,et al. Adaptive Constraint Satisfaction: The Quickest First Principle , 1996, ECAI.
[20] David Robertson,et al. Proceedings of the 12th European Conference on Artificial Intelligence , 1996 .
[21] Corso Elvezia. Probabilistic Incremental Program Evolution , 1997 .
[22] Roberto Battiti,et al. Reactive search, a history-sensitive heuristic for MAX-SAT , 1997, JEAL.
[23] Chu Min Li,et al. Heuristics Based on Unit Propagation for Satisfiability Problems , 1997, IJCAI.
[24] Rafal Salustowicz,et al. Probabilistic Incremental Program Evolution , 1997, Evolutionary Computation.
[25] F. Post,et al. An Economics Approach to Hard Computational Problems , 1997 .
[26] Tad Hogg,et al. An Economics Approach to Hard Computational Problems , 1997, Science.
[27] Fernando G. Lobo,et al. A parameter-less genetic algorithm , 1999, GECCO.
[28] Toby Walsh,et al. The Search for Satisfaction , 1999 .
[29] Hilan Bensusan,et al. Meta-Learning by Landmarking Various Learning Algorithms , 2000, ICML.
[30] Thomas Stützle,et al. SATLIB: An Online Resource for Research on SAT , 2000 .
[31] Michail G. Lagoudakis,et al. Algorithm Selection using Reinforcement Learning , 2000, ICML.
[32] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[33] Pat Langley,et al. Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29 - July 2, 2000 , 2000, ICML 2000.
[34] M. Akritas,et al. Estimation of the conditional distribution in regression with censored data: a comparative study , 2001 .
[35] Luc De Raedt,et al. Proceedings of the 12th European Conference on Machine Learning , 2001 .
[36] Bart Selman,et al. Algorithm portfolios , 2001, Artif. Intell..
[37] Y. Freund,et al. The non-stochastic multi-armed bandit problem , 2001 .
[38] David Maxwell Chickering,et al. A Bayesian Approach to Tackling Hard Computational Problems (Preliminary Report) , 2001, Electron. Notes Discret. Math..
[39] Eric Horvitz,et al. Computational tradeoffs under bounded resources , 2001, Artif. Intell..
[40] Shlomo Zilberstein,et al. Monitoring and control of anytime algorithms: A dynamic programming approach , 2001, Artif. Intell..
[41] Nicolas Barnier,et al. Solving the Kirkman's schoolgirl problem in a few seconds , 2002 .
[42] Peter Auer,et al. The Nonstochastic Multiarmed Bandit Problem , 2002, SIAM J. Comput..
[43] M. May. Bayesian Survival Analysis. , 2002 .
[44] Yoav Shoham,et al. Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions , 2002, CP.
[45] Thomas Stützle,et al. A Racing Algorithm for Configuring Metaheuristics , 2002, GECCO.
[46] Eric Horvitz,et al. Dynamic restart policies , 2002, AAAI/IAAI.
[47] Booncharoen Sirinaovakul,et al. Introduction to the Special Issue , 2002, Comput. Intell..
[48] J. van Leeuwen,et al. Principles and Practice of Constraint Programming - CP 2002 , 2002, Lecture Notes in Computer Science.
[49] R. Solomonoff. Progress In Incremental Machine Learning , 2003 .
[50] James M. Robins,et al. Unified Methods for Censored Longitudinal Data and Causality , 2003 .
[51] Carlos Soares,et al. A Meta-Learning Method to Select the Kernel Width in Support Vector Regression , 2004, Machine Learning.
[52] Mark Wallace,et al. Principles and Practice of Constraint Programming – CP 2004 , 2004, Lecture Notes in Computer Science.
[53] Marek Petrik. Statistically Optimal Combination of Algorithms , 2004 .
[54] Jürgen Schmidhuber,et al. Optimal Ordered Problem Solver , 2002, Machine Learning.
[55] Dino Pedreschi,et al. Machine Learning: ECML 2004 , 2004, Lecture Notes in Computer Science.
[56] Jürgen Schmidhuber,et al. Adaptive Online Time Allocation to Search Algorithms , 2004, ECML.
[57] Jürgen Schmidhuber,et al. Shifting Inductive Bias with Success-Story Algorithm, Adaptive Levin Search, and Incremental Self-Improvement , 1997, Machine Learning.
[58] Bart Selman,et al. Heavy-Tailed Phenomena in Satisfiability and Constraint Satisfaction Problems , 2000, Journal of Automated Reasoning.
[59] Thomas Stützle,et al. Local Search Algorithms for SAT: An Empirical Evaluation , 2000, Journal of Automated Reasoning.
[60] Ricardo Vilalta,et al. Introduction to the Special Issue on Meta-Learning , 2004, Machine Learning.
[61] Thomas Stützle,et al. Stochastic Local Search: Foundations & Applications , 2004 .
[62] J. Christopher Beck,et al. Simple Rules for Low-Knowledge Algorithm Selection , 2004, CPAIOR.
[63] Yoav Shoham,et al. Understanding Random SAT: Beyond the Clauses-to-Variables Ratio , 2004, CP.
[64] Stephen F. Smith,et al. Heuristic Selection for Stochastic Search Optimization: Modeling Solution Quality by Extreme Value Theory , 2004, CP.
[65] Stephen F. Smith,et al. The Max K-Armed Bandit: A New Model of Exploration Applied to Search Heuristic Selection , 2005, AAAI.
[66] Chu Min Li,et al. Diversification and Determinism in Local Search for Satisfiability , 2005, SAT.
[67] Ricardo Vilalta,et al. A Perspective View and Survey of Meta-Learning , 2002, Artificial Intelligence Review.
[68] Y. Shoham,et al. Empirical approach to the complexity of hard problems , 2005 .
[69] Frank Hutter,et al. Parameter Adjustment Based on Performance Prediction: Towards an Instance-Aware Problem Solver , 2005 .
[70] Jürgen Schmidhuber,et al. A Neural Network Model for Inter-problem Adaptive Online Time Allocation , 2005, ICANN.
[71] Wayne B. Nelson,et al. Applied Life Data Analysis: Nelson/Applied Life Data Analysis , 2005 .
[72] J. Christopher Beck,et al. APPLYING MACHINE LEARNING TO LOW‐KNOWLEDGE CONTROL OF OPTIMIZATION ALGORITHMS , 2005, Comput. Intell..
[73] Kevin Leyton-Brown,et al. Performance Prediction and Automated Tuning of Randomized and Parametric Algorithms , 2006, CP.
[74] J. Schmidhuber,et al. Gambling in a Computationally Expensive Casino : Algorithm Selection as a Bandit Problem , 2006 .
[75] Stephen F. Smith,et al. An Asymptotically Optimal Algorithm for the Max k-Armed Bandit Problem , 2006, AAAI.
[76] Jürgen Schmidhuber,et al. Impact of Censored Sampling on the Performance of Restart Strategies , 2006, CP.
[77] Hongzhe Li. Censored Data Regression in High-Dimension and Low-Sample Size Settings For Genomic Applications , 2006 .
[78] Marek Petrik,et al. Learning Static Parallel Portfolios of Algorithms , 2006, ISAIM.
[79] Jürgen Schmidhuber,et al. Dynamic Algorithm Portfolios , 2006, AI&M.
[80] Frédéric Benhamou. Principles and Practice of Constraint Programming - CP 2006, 12th International Conference, CP 2006, Nantes, France, September 25-29, 2006, Proceedings , 2006, CP.
[81] Marek Petrik,et al. Learning parallel portfolios of algorithms , 2006, Annals of Mathematics and Artificial Intelligence.
[82] Jürgen Schmidhuber,et al. Learning Restart Strategies , 2007, IJCAI.
[83] Laura Wichert,et al. Application of a Simple Nonparametric Conditional Quantile Function Estimator in Unemployment Duration Analysis , 2007 .
[84] H. Robbins. Some aspects of the sequential design of experiments , 1952 .
[85] Laura Spierdijk,et al. Nonparametric conditional hazard rate estimation: A local linear approach , 2008, Comput. Stat. Data Anal..
[86] Marvin Rausand,et al. Life Data Analysis , 2008 .
[87] David W. Hosmer,et al. Applied Survival Analysis: Regression Modeling of Time-to-Event Data , 2008 .