Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon
暂无分享,去创建一个
Yoshua Bengio | Andrea Lodi | Antoine Prouvost | Yoshua Bengio | A. Lodi | Antoine Prouvost | Andrea Lodi
[1] Kevin Tierney,et al. Deep Learning Assisted Heuristic Tree Search for the Container Pre-marshalling Problem , 2017, Comput. Oper. Res..
[2] Andrea Lodi,et al. Exact Combinatorial Optimization with Graph Convolutional Neural Networks , 2019, NeurIPS.
[3] David L. Dill,et al. Learning a SAT Solver from Single-Bit Supervision , 2018, ICLR.
[4] Peter Stone,et al. Deterministic Implementations for Reproducibility in Deep Reinforcement Learning , 2018, ArXiv.
[5] Yoshua Bengio,et al. Predicting Solution Summaries to Integer Linear Programs under Imperfect Information with Machine Learning , 2018, ArXiv.
[6] Timothy C. Y. Chan,et al. Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks , 2018, MLHC.
[7] Michele Lombardi,et al. Boosting Combinatorial Problem Modeling with Machine Learning , 2018, IJCAI.
[8] Carlos Ansótegui,et al. Hyper-Reactive Tabu Search for MaxSAT , 2018, LION.
[9] Sanjay Ranka,et al. Learning Permutations with Sinkhorn Policy Gradient , 2018, ArXiv.
[10] Marco Molinaro,et al. Theoretical challenges towards cutting-plane selection , 2018, Math. Program..
[11] Wouter Kool,et al. Attention Solves Your TSP, Approximately , 2018 .
[12] Max Welling,et al. Attention Solves Your TSP , 2018, ArXiv.
[13] Andrea Lodi,et al. Learning a Classification of Mixed-Integer Quadratic Programming Problems , 2017, CPAIOR.
[14] Pietro Liò,et al. Graph Attention Networks , 2017, ICLR.
[15] Tom White,et al. Generative Adversarial Networks: An Overview , 2017, IEEE Signal Processing Magazine.
[16] Marius Lindauer,et al. Warmstarting of Model-based Algorithm Configuration , 2017, AAAI.
[17] Iain Dunning,et al. Learning Fast Optimizers for Contextual Stochastic Integer Programs , 2018, UAI.
[18] George L. Nemhauser,et al. Learning to Run Heuristics in Tree Search , 2017, IJCAI.
[19] Joan Bruna,et al. A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks , 2017, ArXiv.
[20] Andrea Lodi,et al. On learning and branching: a survey , 2017 .
[21] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[22] Marco E. Lübbecke,et al. Learning When to Use a Decomposition , 2017, CPAIOR.
[23] Mohamed Medhat Gaber,et al. Imitation Learning , 2017, ACM Comput. Surv..
[24] Le Song,et al. 2 Common Formulation for Greedy Algorithms on Graphs , 2018 .
[25] Samuel S. Schoenholz,et al. Neural Message Passing for Quantum Chemistry , 2017, ICML.
[26] Misha Denil,et al. Learned Optimizers that Scale and Generalize , 2017, ICML.
[27] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[28] Jitendra Malik,et al. Learning to Optimize Neural Nets , 2017, ArXiv.
[29] Louis Wehenkel,et al. A Machine Learning-Based Approximation of Strong Branching , 2017, INFORMS J. Comput..
[30] Samy Bengio,et al. Neural Combinatorial Optimization with Reinforcement Learning , 2016, ICLR.
[31] Hugo Larochelle,et al. Optimization as a Model for Few-Shot Learning , 2016, ICLR.
[32] Abraham P. Punnen,et al. Markov Chain methods for the Bipartite Boolean Quadratic Programming Problem , 2017, Eur. J. Oper. Res..
[33] Carlos Ansótegui,et al. Reactive Dialectic Search Portfolios for MaxSAT , 2017, AAAI.
[34] C A Nelson,et al. Learning to Learn , 2017, Encyclopedia of Machine Learning and Data Mining.
[35] Marcin Andrychowicz,et al. Learning to learn by gradient descent by gradient descent , 2016, NIPS.
[36] Barry O'Sullivan,et al. Structure-Preserving Instance Generation , 2016, LION.
[37] Le Song,et al. Discriminative Embeddings of Latent Variable Models for Structured Data , 2016, ICML.
[38] Le Song,et al. Learning to Branch in Mixed Integer Programming , 2016, AAAI.
[39] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[40] Bernd Bischl,et al. ASlib: A benchmark library for algorithm selection , 2015, Artif. Intell..
[41] Yuri Malitsky,et al. DASH: Dynamic Approach for Switching Heuristics , 2013, Eur. J. Oper. Res..
[42] Louis Wehenkel,et al. Online Learning for Strong Branching Approximation in Branch-and-Bound , 2016 .
[43] Kate Smith-Miles,et al. Generating new test instances by evolving in instance space , 2015, Comput. Oper. Res..
[44] Navdeep Jaitly,et al. Pointer Networks , 2015, NIPS.
[45] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[46] Yoshua Bengio,et al. Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.
[47] He He,et al. Learning to Search in Branch and Bound Algorithms , 2014, NIPS.
[48] Thomas Stützle,et al. Grammar-based generation of stochastic local search heuristics through automatic algorithm configuration tools , 2014, Comput. Oper. Res..
[49] Barry O'Sullivan,et al. ReACT: Real-Time Algorithm Configuration through Tournaments , 2014, SOCS.
[50] Timothy C. Y. Chan,et al. Generalized Inverse Multiobjective Optimization with Application to Cancer Therapy , 2014, Oper. Res..
[51] Louis Wehenkel,et al. A Supervised Machine Learning Approach to Variable Branching in Branch-And-Bound , 2014 .
[52] Kevin P. Murphy,et al. Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.
[53] Holger H. Hoos,et al. Automated Algorithm Configuration and Parameter Tuning , 2012, Autonomous Search.
[54] A. Lodi,et al. Heuristics in Mixed Integer Programming , 2011 .
[55] Jürgen Schmidhuber,et al. Recurrent policy gradients , 2010, Log. J. IGPL.
[56] Michel Gendreau,et al. Handbook of Metaheuristics , 2010 .
[57] Edmund K. Burke,et al. A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling , 2010, Int. J. Appl. Metaheuristic Comput..
[58] William J. Cook,et al. The Traveling Salesman Problem: A Computational Study , 2007 .
[59] Nasser M. Nasrabadi,et al. Pattern Recognition and Machine Learning , 2006, Technometrics.
[60] David L. Applegate,et al. The traveling salesman problem , 2006 .
[61] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[62] M. Rao,et al. Technical Report Integer Programming 1 Integer Programming 4 , 1998 .
[63] Ravindra K. Ahuja,et al. Inverse Optimization , 2001, Oper. Res..
[64] Sepp Hochreiter,et al. Learning to Learn Using Gradient Descent , 2001, ICANN.
[65] Kate A. Smith,et al. Neural Networks for Combinatorial Optimization: a Review of More Than a Decade of Research , 1999 .
[66] Sebastian Thrun,et al. Learning to Learn , 1998, Springer US.
[67] M. Fortun,et al. Scientists and the Legacy of World War II: The Case of Operations Research (OR) , 1993 .
[68] Jürgen Schmidhuber,et al. Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks , 1992, Neural Computation.
[69] Yoshua Bengio,et al. Learning a synaptic learning rule , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.
[70] Richard C. Larson,et al. Urban Operations Research , 1981 .
[71] Garth P. McCormick,et al. Computability of global solutions to factorable nonconvex programs: Part I — Convex underestimating problems , 1976, Math. Program..
[72] G. Nemhauser,et al. Integer Programming , 2020 .