We would like to maintain a list of resources that utilize machine learning technologies to solve combinatorial optimization problems.
We mark work contributed by Thinklab with ⭐.
Maintained by members in SJTU-Thinklab: Chang Liu, Runzhong Wang, Jiayi Zhang, Zelin Zhao, Haoyu Geng, Tianzhe Wang, Wenxuan Guo, Wenjie Wu, Nianzu Yang and Junchi Yan. We also thank all contributers from the community!
We are looking for post-docs interested in machine learning especially for learning combinatorial solvers, dynamic graphs, and reinforcement learning. Please send your up-to-date resume via yanjunchi AT sjtu.edu.cn.
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Neural Networks for Combinatorial Optimization: A Review of More Than a Decade of Research INFORMS Journal on Computing, 1999. journal
Smith, Kate A.
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Model-Based Search for Combinatorial Optimization: A Critical Survey. Annals of Operations Research, 2004. journal
Zlochin, Mark and Birattari, Mauro and Meuleau, Nicolas and Dorigo, Marco.
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A Survey of Reinforcement Learning and Agent-Based Approaches to Combinatorial Optimization. Citeseer, 2012. journal
Miagkikh, Victor
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Machine Learning Approaches to Learning Heuristics for Combinatorial Optimization Problems. Procedia Manufacturing, 2018. journal
Mirshekarian, Sadegh and Sormaz, Dusan.
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Boosting combinatorial problem modeling with machine learning. IJCAI, 2018. paper
Lombardi, Michele and Milano, Michela.
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Deep Reinforcement Learning as a Job Shop Scheduling Solver: A Literature Review Hybrid Intelligent Systems, 2018. journal
Bruno Cunha, Ana M. Madureira, Benjamim Fonseca, Duarte Coelho
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A Review of combinatorial optimization with graph neural networks. BigDIA, 2019. paper
Huang, Tingfei and Ma, Yang and Zhou, Yuzhen and Huang, Honglan Huang and Chen, Dongmei and Gong, Zidan and Liu, Yao.
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Machine Learning for Combinatorial Optimization: a Methodological Tour d'horizon. EJOR, 2020. journal
Bengio, Yoshua and Lodi, Andrea and Prouvost, Antoine.
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Reinforcement Learning for Combinatorial Optimization: A Survey. Arxiv, 2020. paper
Mazyavkina, Nina and Sviridov, Sergey and Ivanov, Sergei and Burnaev, Evgeny.
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⭐Learning Graph Matching and Related Combinatorial Optimization Problems. IJCAI, 2020. paper
Yan, Junchi and Yang, Shuang, and Hancock, Edwin R.
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Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking. IEEE ACCESS, 2020. journal
Vesselinova, Natalia and Steinert, Rebecca and Perez-Ramirez, Daniel F. and Boman, Magnus.
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From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning. Arxiv, 2020. paper
Bouraoui, Zied and Cornuéjols, Antoine and Denœux, Thierry and Destercke, Sébastien and Dubois, Didier and Guillaume, Romain and Marques-Silva, João and Mengin, Jérôme and Prade, Henri and Schockaert, Steven and Serrurier, Mathieu and Vrain, Christel.
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A Survey on Reinforcement Learning for Combinatorial Optimization. Arxiv, 2020. paper
Yang, Yunhao and Whinston, Andrew.
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Research Reviews of Combinatorial Optimization Methods Based on Deep Reinforcement Learning. (in chinese) 自动化学报, 2020. journal
Li, Kai-Wen and Zhang, Tao and Wang, Rui and Qin, Wei-Jian and He, Hui-Hui and Huang, Hong.
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Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art. Data Science and Engineering, 2021. journal
Peng, Yue, Choi, Byron, and Xu, Jianliang.
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Combinatorial Optimization and Reasoning with Graph Neural Networks Arxiv, 2021. paper
Cappart, Quentin and Chetelat, Didier and Khalil, Elias and Lodi, Andrea and Morris, Christopher and Velickovic, Petar
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Machine Learning for Electronic Design Automation (EDA) : A Survey TODAES, 2021. journal
Huang, Guyue and Hu, Jingbo and He, Yifan and Liu, Jialong and Ma, Mingyuan and Shen, Zhaoyang and Wu, Juejian and Xu, Yuanfan and Zhang, Hengrui and Zhong, Kai and others
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⭐A Survey for Solving Mixed Integer Programming via Machine Learning Neurocomputing, 2022. journal
Jiayi Zhang and Chang Liu and Xijun Li and Hui-Ling Zhen and Mingxuan Yuan and Yawen Li and Junchi Yan
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Revised Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks Arxiv, 2017. paper, code
Nowak, Alex and Villar, Soledad and Bandeira, S. Afonso and Bruna, Joan
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Deep Learning of Graph Matching. CVPR, 2018. paper
Zanfir, Andrei and Sminchisescu, Cristian
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⭐Learning Combinatorial Embedding Networks for Deep Graph Matching. ICCV, 2019. paper, code
Wang, Runzhong and Yan, Junchi and Yang, Xiaokang
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Deep Graphical Feature Learning for the Feature Matching Problem. ICCV, 2019. paper
Zhang, Zhen and Lee, Wee Sun
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GLMNet: Graph Learning-Matching Networks for Feature Matching. Arxiv, 2019. paper
Jiang, Bo and Sun, Pengfei and Tang, Jin and Luo, Bin
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⭐Learning deep graph matching with channel-independent embedding and Hungarian attention. ICLR, 2020. paper, code
Yu, Tianshu and Wang, Runzhong and Yan, Junchi and Li, Baoxin
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Deep Graph Matching Consensus. ICLR, 2020. paper
Fey, Matthias and Lenssen, Jan E. and Morris, Christopher and Masci, Jonathan and Kriege, Nils M.
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⭐Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning. NeurIPS, 2020. paper, code
Wang, Runzhong and Yan, Junchi and Yang, Xiaokang
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⭐Combinatorial Learning of Robust Deep Graph Matching: An Embedding Based Approach. TPAMI, 2020. paper, code
Wang, Runzhong and Yan, Junchi and Yang, Xiaokang
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Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers. ECCV, 2020. paper, code
Rolinek, Michal and Swoboda, Paul and Zietlow, Dominik and Paulus, Anselm and Musil, Vit and Martius, Georg
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⭐Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching. TPAMI, 2021. paper, code
Wang, Runzhong and Yan, Junchi and Yang, Xiaokang
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⭐Deep Latent Graph Matching ICML, 2021. paper
Yu, Tianshu and Wang, Runzhong and Yan, Junchi and Li, Baoxin.
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IA-GM: A Deep Bidirectional Learning Method for Graph Matching AAAI, 2021. paper
Zhao, Kaixuan and Tu, Shikui and Xu, Lei
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Deep Graph Matching under Quadratic Constraint CVPR, 2021. paper
Gao, Quankai and Wang, Fudong and Xue, Nan and Yu, Jin-Gang and Xia, Gui-Song
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GAMnet: Robust Feature Matching via Graph Adversarial-Matching Network MM, 2021. paper
Jiang, Bo and Sun, Pengfei and Zhang, Ziyan and Tang, Jin and Luo, Bin
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Hypergraph Neural Networks for Hypergraph Matching ICCV, 2021. paper
Liao, Xiaowei and Xu, Yong and Ling, Haibin
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Learning to Match Features with Seeded Graph Matching Network ICCV, 2021. paper
Chen, Hongkai and Luo, Zixin and Zhang, Jiahui and Zhou, Lei and Bai, Xuyang and Hu, Zeyu and Tai, Chiew-Lan and Quan, Long
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⭐Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond CVPR, 2022. paper, code
Ren, Qibing and Bao, Qingquan and Wang, Runzhong and Yan, Junchi
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⭐Self-supervised Learning of Visual Graph Matching ECCV, 2022. paper, code
Liu, Chang and Zhang, Shaofeng and Yang, Xiaokang and Yan, Junchi
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⭐Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching. ICLR, 2023. paper, code
Liu, Chang and Jiang, Zetian and Wang, Runzhong and Yan, Junchi and Huang, Lingxiao and Lu, Pinyan
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Revised Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks Arxiv, 2017. paper, code
Nowak, Alex and Villar, Soledad and Bandeira, S. Afonso and Bruna, Joan
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⭐Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching. TPAMI, 2021. paper, code
Wang, Runzhong and Yan, Junchi and Yang, Xiaokang
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⭐Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching. ICLR, 2023. paper, code
Liu, Chang and Jiang, Zetian and Wang, Runzhong and Yan, Junchi and Huang, Lingxiao and Lu, Pinyan
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Learning Combinatorial Optimization Algorithms over Graphs. NeurIPS, 2017. paper
Dai, Hanjun and Khalil, Elias B and Zhang, Yuyu and Dilkina, Bistra and Song, Le
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Learning Heuristics for the TSP by Policy Gradient CPAIOR, 2018. paper, code
Michel DeudonPierre CournutAlexandre Lacoste
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Attention, Learn to Solve Routing Problems! ICLR, 2019. paper
Kool, Wouter and Van Hoof, Herke and Welling, Max.
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Learning to Solve NP-Complete Problems: A Graph Neural Network for Decision TSP. AAAI, 2019. paper
Prates, Marcelo and Avelar, Pedro HC and Lemos, Henrique and Lamb, Luis C and Vardi, Moshe Y.
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An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem Arxiv, 2019. paper, code
Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson
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POMO: Policy Optimization with Multiple Optima for Reinforcement Learning. NeurIPS, 2020. paper, code
Kwon, Yeong-Dae and Choo, Jinho and Kim, Byoungjip and Yoon, Iljoo and Min, Seungjai and Gwon, Youngjune.
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Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances. Arxiv, 2020. paper
Fu, Zhang-Hua and Qiu, Kai-Bin and Zha, Hongyuan.
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A Reinforcement Learning Approach for Optimizing Multiple Traveling Salesman Problems over Graphs KBS, 2020. journal
Hu, Yujiao and Yao, Yuan and Lee, Wee Sun
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Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning ACML, 2020. paper, code
d O Costa, Paulo R and Rhuggenaath, Jason and Zhang, Yingqian and Akcay, Alp
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Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems. IEEE Trans Cybern, 2021. journal
Kaiwen Li, Tao Zhang, Rui Wang Yuheng Wang, and Yi Han
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The Transformer Network for the Traveling Salesman Problem IPAM, 2021. paper
Xavier Bresson,Thomas Laurent
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Learning Improvement Heuristics for Solving Routing Problems TNNLS, 2021. journal
Wu, Yaoxin and Song, Wen and Cao, Zhiguang and Zhang, Jie and Lim, Andrew
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Reversible Action Design for Combinatorial Optimization with Reinforcement Learning Arxiv, 2021. paper
Yao, Fan and Cai, Renqin and Wang, Hongning
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Solving Dynamic Traveling Salesman Problems with Deep Reinforcement Learning. TNNLS, 2021. journal
Zizhen Zhang, Hong Liu, Meng Chu Zhou, Jiahai Wang
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ScheduleNet: Learn to Solve Multi-agent Scheduling Problems with Reinforcement Learning Arxiv, 2021. paper
Junyoung Park, Sanjar Bakhtiyar, Jinkyoo Park
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DAN: Decentralized Attention-based Neural Network to Solve the MinMax Multiple Traveling Salesman Problem Arxiv, 2021. paper
Cao, Yuhong and Sun, Zhanhong and Sartoretti, Guillaume
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Reinforcement Learning for Route Optimization with Robustness Guarantees IJCAI, 2021. paper
Jacobs, Tobias and Alesiani, Francesco and Ermis, Gulcin
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Learning TSP Requires Rethinking Generalization CP, 2021. paper, code
Chaitanya K. Joshi, Quentin Cappart, Louis-Martin Rousseau and Thomas Laurent
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The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems Arxiv, 2022. paper, code
Bliek, Laurens and da Costa, Paulo and Afshar, Reza Refaei and Zhang, Yingqian and Catshoek, Tom and Vos, Daniel and Verwer, Sicco and Schmitt-Ulms, Fynn and Hottung, Andre and Shah, Tapan and others
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Graph Neural Network Guided Local Search for the Traveling Salesperson Problem ICLR, 2022. paper
Hudson, Benjamin and Li, Qingbiao and Malencia, Matthew and Prorok, Amanda
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Preference Conditioned Neural Multi-objective Combinatorial Optimization ICLR, 2022. paper
Lin, Xi and Yang, Zhiyuan and Zhang, Qingfu
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Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation NeurIPS, 2022. paper, code
Bi, Jieyi and Ma, Yining and Wang, Jiahai and Cao, Zhiguang and Chen, Jinbiao and Sun, Yuan and Chee, Yeow Meng
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DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems NeurIPS, 2022. paper
Qiu, Ruizhong and Sun, Zhiqing and Yang, Yiming
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Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization NeurIPS, 2022. paper, code
Kim, Minsu and Park, Junyoung and Park, Jinkyoo
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Simulation-guided Beam Search for Neural Combinatorial Optimization NeurIPS, 2022. paper, code
Choo, Jinho and Kwon, Yeong-Dae and Kim, Jihoon and Jae, Jeongwoo and Hottung, Andr{'e} and Tierney, Kevin and Gwon, Youngjune
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Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness ICLR, 2022. paper
Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski and Stephan Günnemann
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Learning to CROSS exchange to solve min-max vehicle routing problems ICLR, 2023. paper
Kim, Minjun and Park, Junyoung and Park, Jinkyoo
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Generalize Learned Heuristics to Solve Large-scale Vehicle Routing Problems in Real-time ICLR, 2023. paper
Hou, Qingchun and Yang, Jingwei and Su, Yiqiang and Wang, Xiaoqing and Deng, Yuming
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⭐ROCO: A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs ICLR, 2023. paper, code
Lu, Han and Li, Zenan and Wang, Runzhong and Ren, Qibing and Li, Xijun and Yuan, Mingxuan and Zeng, Jia and Yang, Xiaokang and Yan, Junchi
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Pointerformer: Deep Reinforced Multi-Pointer Transformer for the Traveling Salesman Problem Arxiv, 2023. paper, code
Yan Jin, Yuandong Ding, Xuanhao Pan, Kun He, Li Zhao, Tao Qin, Lei Song, Jiang Bian
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H-tsp: Hierarchically solving the large-scale traveling salesman problem AAAI, 2023. paper, code
Xuanhao Pan, Yan Jin, Yuandong Ding, Mingxiao Feng, Li Zhao, Lei Song, Jiang Bian
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Select and Optimize: Learning to solve large-scale TSP instances AISTATS, 2023. paper
Hanni Cheng, Haosi Zheng, Ya Cong, Weihao Jiang, Shiliang Pu
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Learning Combinatorial Optimization Algorithms over Graphs. NeurIPS, 2017. paper
Dai, Hanjun and Khalil, Elias B and Zhang, Yuyu and Dilkina, Bistra and Song, Le
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Exploratory Combinatorial Optimization with Reinforcement Learning. AAAI, 2020. paper
LBarrett, Thomas and Clements, William and Foerster, Jakob and Lvovsky, Alex.
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Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs. NeurIPS, 2020. paper
Karalias, Nikolaos and Loukas, Andreas
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Reversible Action Design for Combinatorial Optimization with Reinforcement Learning Arxiv, 2021. paper
Yao, Fan and Cai, Renqin and Wang, Hongning
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LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation ICML, 2022. paper, code
Ireland, David and G. Montana
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Learning to Solve Combinatorial Graph Partitioning Problems via Efficient Exploration Arxiv, 2022. paper, code
Barrett, Thomas D and Parsonson, Christopher WF and Laterre, Alexandre
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Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets Arxiv, 2023. paper
Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron Courville, Yoshua Bengio, Ling Pan
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Differentiable Learning of Submodular Models NeurIPS, 2017. paper, code
Josip Djolonga, Andreas Krause
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OptNet: differentiable optimization as a layer in neural networks ICML, 2017. paper, code
Brandon Amos and J. Zico Kolter
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Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization AAAI, 2019. paper
Bryan Wilder, Bistra Dilkina, Milind Tambe
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Differentiable Convex Optimization Layers NeurIPS, 2019. paper, code
Agrawal, Akshay and Boyd, Stephen
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Predict+optimise with ranking objectives: exhaustively learning linear functions IJCAI, 2019. paper
Demirovic, Emir and Stuckey, Peter J. and Bailey, James and Chan, Jeffrey and Leckie, Christopher and Ramamohanarao, Kotagiri and Guns, Tias
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Differentiation of Blackbox Combinatorial Solvers ICLR, 2020. paper, code
Marin Vlastelica, Anselm Paulus, Vít Musil, Georg Martius, Michal Rolínek
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MIPaaL: Mixed Integer Program as a Layer AAAI, 2020. paper, code
Aaron Ferber, Bryan Wilder, Bistra Dilkina, Milind Tambe
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Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems AAAI, 2020. paper, code
Jaynta Mandi, Emir Demirovi, Peter. J Stuckey, Tias Guns
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Differentiation of blackbox combinatorial solvers ICLR, 2020. paper, code
Marin Vlastelica Pogani, Anselm Paulus, Vit Musil, Georg Martius, Michal Rolinek
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Interior Point Solving for LP-based prediction+optimization NeurIPS, 2020. paper, code
Jayanta Mandi, Tias Guns
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Automatically Learning Compact Quality-aware Surrogates for Optimization Problems NeurIPS, 2020. paper
Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe
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Contrastive Losses and Solution Caching for Predict-and-Optimize IJCAI, 2021. paper, code
Maxime Mulamba, Jayanta Mandi, Michelangelo Diligenti , Michele Lombardi, Victor Bucarey, Tias Guns
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A Surrogate Objective Framework for Prediction+Programming with Soft Constraints NeurIPS, 2021. paper, code
Kai Yan, Jie Yan, Chuan Luo, Liting Chen, Qingwei Lin, Dongmei Zhang
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Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions NeurIPS, 2021. paper, code
Mathias Niepert, Pasquale Minervini, Luca Franceschi
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COPS: Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable Approach NeurIPS, 2021. paper, code
Ahmed Abbas, Paul Swoboda
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An Exact Symbolic Reduction of Linear Smart Predict+Optimize to Mixed Integer Linear Programming ICML, 2022. paper, code
Jeong, Jihwan, Parth Jaggi, Andrew Butler and Scott Sanner. “An Exact Symbolic Reduction of Linear Smart Predict+Optimize to Mixed Integer Linear Programming.” ICML (2022).
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Constrained Discrete Black-Box Optimization using Mixed-Integer Programming ICML, 2022. paper
Papalexopoulos, Theodore, Christian Tjandraatmadja, Ross Anderson, Juan Pablo Vielma and Daving Belanger.
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End-to-End Stochastic Optimization with Energy-Based Model NeurIPS, 2022. paper, code
Lingkai Kong, Jiaming Cui, Yuchen Zhuang, Rui Feng, B. Aditya Prakash, Chao Zhang
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Deep Declarative Networks TPAMI, 2022. paper, code
Stephen Gould, Richard Hartley and Dylan Campbell
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Learning to Perform Local Rewriting for Combinatorial Optimization. NeurIPS, 2019. paper, code
Chen, Xinyun and Tian, Yuandong.
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Deep Reinforcement Learning for the Electric Vehicle Routing Problem with Time Windows. Arxiv, 2020. paper
Lin, Bo and Ghaddar, Bissan and Nathwani, Jatin.
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Efficiently Solving the Practical,Vehicle Routing Problem: A Novel Joint Learning Approach. KDD, 2020. paper
Lu Duan, Yang Zhan, Haoyuan Hu, Yu Gong, Jiangwen Wei, Xiaodong Zhang, Yinghui Xu
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Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing NeurIPS, 2020. paper, code
Arthur Delarue, Ross Anderson, Christian Tjandraatmadja
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A Learning-based Iterative Method for Solving Vehicle Routing Problems ICLR, 2020. paper
Lu, Hao and Zhang, Xingwen and Yang, Shuang
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Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problem Arxiv, 2020. paper
Hottung, Andre and Tierney, Kevin
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Learning Improvement Heuristics for Solving Routing Problems TNNLS, 2021. journal
Wu, Yaoxin and Song, Wen and Cao, Zhiguang and Zhang, Jie and Lim, Andrew
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Reinforcement Learning for Route Optimization with Robustness Guarantees IJCAI, 2021. paper
Jacobs, Tobias and Alesiani, Francesco and Ermis, Gulcin
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Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems. AAAI, 2021. paper, code
Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang
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Analytics and Machine Learning in Vehicle Routing Research Arxiv, 2021. paper
Bai, Ruibin and Chen, Xinan and Chen, Zhi-Long and Cui, Tianxiang and Gong, Shuhui and He, Wentao and Jiang, Xiaoping and Jin, Huan and Jin, Jiahuan and Kendall, Graham and others
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RP-DQN: An application of Q-Learning to Vehicle Routing Problems Arxiv, 2021. paper
Bdeir, Ahmad and Boeder, Simon and Dernedde, Tim and Tkachuk, Kirill and Falkner, Jonas K and Schmidt-Thieme, Lars
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Deep Policy Dynamic Programming for Vehicle Routing Problems Arxiv, 2021. paper
Kool, Wouter and van Hoof, Herke and Gromicho, Joaquim and Welling, Max
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Learning to Delegate for Large-scale Vehicle Routing NeurIPS, 2021. paper
Li, Sirui and Yan, Zhongxia and Wu, Cathy
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Learning a Latent Search Space for Routing Problems using Variational Autoencoders ICLR, 2021. paper
Hottung, Andre and Bhandari, Bhanu and Tierney, Kevin
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Preference Conditioned Neural Multi-objective Combinatorial Optimization ICLR, 2022. paper
Lin, Xi and Yang, Zhiyuan and Zhang, Qingfu
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Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation NeurIPS, 2022. paper, code
Bi, Jieyi and Ma, Yining and Wang, Jiahai and Cao, Zhiguang and Chen, Jinbiao and Sun, Yuan and Chee, Yeow Meng
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Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization NeurIPS, 2022. paper, code
Kim, Minsu and Park, Junyoung and Park, Jinkyoo
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Simulation-guided Beam Search for Neural Combinatorial Optimization NeurIPS, 2022. paper, code
Choo, Jinho and Kwon, Yeong-Dae and Kim, Jihoon and Jae, Jeongwoo and Hottung, Andr{'e} and Tierney, Kevin and Gwon, Youngjune
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Learning to CROSS exchange to solve min-max vehicle routing problems ICLR, 2023. paper
Kim, Minjun and Park, Junyoung and Park, Jinkyoo
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Generalize Learned Heuristics to Solve Large-scale Vehicle Routing Problems in Real-time ICLR, 2023. paper
Hou, Qingchun and Yang, Jingwei and Su, Yiqiang and Wang, Xiaoqing and Deng, Yuming
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Smart Manufacturing Scheduling With Edge Computing Using Multiclass Deep Q Network Transactions on Industrial Informatics, 2019. journal
Chun-Cheng Lin, Der-Jiunn Deng, Yen-Ling Chih, Hsin-Ting Chiu
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Multi-Agent Reinforcement Learning for Job Shop Scheduling in Flexible Manufacturing Systems International Conference on Artificial Intelligence for Industries (AI4I), 2019. paper
Schirin Baer, Jupiter Bakakeu, Richard Meyes, Tobias Meisen
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Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning. NeurIPS, 2020. paper, code
Zhang, Cong and Song, Wen and Cao, Zhiguang and Zhang, Jie and Tan, Puay Siew and Xu, Chi.
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ScheduleNet: Learn to Solve Multi-agent Scheduling Problems with Reinforcement Learning Arxiv, 2021. paper
Junyoung Park, Sanjar Bakhtiyar, Jinkyoo Park
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Dynamic job-shop scheduling in smart manufacturing using deep reinforcement learning Computer Networks, 2021. journal
Libing Wang, Xin Hu, Yin Wang, Sujie Xu, Shijun Ma, Kexin Yang, Zhijun Liu, Weidong Wang
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Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning. International Journal of Production Research, 2021. journal
Junyoung Park, Jaehyeong Chun, Sang Hun Kim, Youngkook Kim, Jinkyoo Park
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Explainable reinforcement learning in production control of job shop manufacturing system. International Journal of Production Research, 2021. journal
Andreas Kuhnle,Marvin Carl May,Louis Sch?fer & Gisela Lanza
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Neural DAG Scheduling via One-Shot Priority Sampling ICLR, 2023. paper
Jeon, Wonseok and Gagrani, Mukul and Bartan, Burak and Zeng, Weiliang Will and Teague, Harris and Zappi, Piero and Lott, Christopher
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Robust Scheduling with GFlowNets ICLR, 2023. paper
Zhang, David W and Rainone, Corrado and Peschl, Markus and Bondesan, Roberto
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Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search. NeurIPS, 2018. paper
Li, Zhuwen and Chen, Qifeng and Koltun, Vladlen.
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Learning What to Defer for Maximum Independent Sets ICML, 2020. paper
Ahn, Sungsoo and Seo, Younggyo and Shin, Jinwoo
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Distributed Scheduling Using Graph Neural Networks ICASSP, 2021. paper
Zhao, Zhongyuan and Verma, Gunjan and Rao, Chirag and Swami, Ananthram and Segarra, Santiago
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Solving Graph-based Public Good Games with Tree Search and Imitation Learning NeurlPS, 2021. paper
Darvariu, Victor-Alexandru and Hailes, Stephen and Musolesi, Mirco
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NN-Baker: A Neural-network Infused Algorithmic Framework for Optimization Problems on Geometric Intersection Graphs NeurlPS, 2021. paper
McCarty, Evan and Zhao, Qi and Sidiropoulos, Anastasios and Wang, Yusu
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What's Wrong with Deep Learning in Tree Search for Combinatorial Optimization ICLR, 2022. paper, code
Bother, Maximilian and Kissig, Otto and Taraz, Martin and Cohen, Sarel and Seidel, Karen and Friedrich, Tobias
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Optimistic tree search strategies for black-box combinatorial optimization NeurlPS, 2022. paper
Malherbe, Cedric and Grosnit, Antoine and Tutunov, Rasul and Ammar, Haitham Bou and Wang, Jun
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⭐ROCO: A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs ICLR, 2023. paper, code
Lu, Han and Li, Zenan and Wang, Runzhong and Ren, Qibing and Li, Xijun and Yuan, Mingxuan and Zeng, Jia and Yang, Xiaokang and Yan, Junchi
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Unsupervised Learning for Combinatorial Optimization Needs Meta Learning ICLR, 2023. paper, code
Wang, Haoyu and Li, Pan
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Graph-based Deterministic Policy Gradient for Repetitive Combinatorial Optimization Problems ICLR, 2023. paper, code
Zhao, Zhongyuan and Swami, Ananthram and Segarra, Santiago
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Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets Arxiv, 2023. paper
Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron Courville, Yoshua Bengio, Ling Pan
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It's Not What Machines Can Learn It's What We Cannot Teach ICML, 2020. paper
Gal Yehuda, Moshe Gabel and Assaf Schuster
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Learning TSP Requires Rethinking Generalization CP, 2021. paper, code
Chaitanya K. Joshi, Quentin Cappart, Louis-Martin Rousseau and Thomas Laurent
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Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness ICLR, 2022. paper
Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski and Stephan Günnemann
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Learning for Robust Combinatorial Optimization: Algorithm and Application INFOCOM, 2022. journal
Shao, Zhihui and Yang, Jianyi and Shen, Cong and Ren, Shaolei
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⭐ROCO: A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs ICLR, 2023. paper, code
Lu, Han and Li, Zenan and Wang, Runzhong and Ren, Qibing and Li, Xijun and Yuan, Mingxuan and Zeng, Jia and Yang, Xiaokang and Yan, Junchi
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Resource Management with Deep Reinforcement Learning. HotNets, 2016. paper
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Stochastic Optimization of Sorting Networks via Continuous Relaxations ICLR, 2019. paper, code
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Optimizing Rank-Based Metrics With Blackbox Differentiation CVPR, 2020. paper, code
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GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination AAAI, 2019. paper, code
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SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinations IJCAI, 2021. paper, code
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Debiased, Longitudinal and Coordinated Drug Recommendation through Multi-Visit Clinic Recordss NeurIPS, 2022. paper, code
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