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Paper_by_learning_paradigms.md

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🦄Hi, welcome COP ZooParty

🐼Reinforcement Learning

  • 【HNC】Hierarchical Neural Constructive Solver for Real-world TSP Scenarios, 2024, SIGKDD.[Paper]
  • 【LR-POMO】Leader Reward for POMO-Based Neural Combinatorial Optimization, 2024, Preprint.[Paper]
  • 【INViT】INViT: A Generalizable Routing Problem Solver with Invariant Nested View Transformer, 2024, ICML.[Paper][Code]
  • Deep Reinforcement Learning for Dynamic Capacitated Vehicle Routing Problem, 2024, ICLR.[Paper]
  • 【ELG】Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policy, 2024, IJCAI.[Paper][Code]
  • 【mEGAT】SYMMETRY-PRESERVING GRAPH ATTENTION NETWORK TO SOLVE ROUTING PROBLEMS AT MULTIPLE RESOLUTIONS, 2024, Preprint. [Paper][Code]
  • 【LCH-Regret】Learning Encodings for Constructive Neural Combinatorial Optimization Needs to Regret, 2024, AAAI.[Paper][Code]
  • 【FER】Learning Feature Embedding Refiner for Solving Vehicle Routing Problems, 2023, TNNLS.[Paper][Code]
  • 【Poppy】Population-based reinforcement learning for combinatorial optimization, 2023, NeurIPS.[Paper][Code]
  • 【COMPASS】Combinatorial Optimization with Policy Adaptation using Latent Space Search, 2023, NeurIPS.[Paper][Code]
  • 【Pointerformer】Pointerformer: Deep Reinforced Multi-Pointer Transformer for the Traveling Salesman Problem, 2023, AAAI.[Paper][Code]
  • 【Tspformer】Memory-efficient Transformer-based network model for Traveling Salesman Problem, 2023, Neural Networks.[Paper][Code]
  • 【H-TSP】H-TSP: Hierarchically Solving the Large-Scale Travelling Salesman Problem, 2023, AAAI.[Paper][Code]
  • 【SO】Select and Optimize: Learning to solve large-scale TSP instances, 2023, AISTATS.[Paper][Code]
  • 【MVGCL】Multi-View Graph Contrastive Learning for Solving Vehicle Routing Problems, 2023, UAI.[Paper]
  • 【RL-CSL】RL-CSL: A Combinatorial Optimization Method Using Reinforcement Learning and Contrastive Self-Supervised Learning, 2023, TETCI.[Paper][Code]
  • 【Sym-NCO】Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization, 2022, NeurIPS.[Paper][Code]
  • 【SADM】Attention, Filling in The Gaps for Generalization in Routing Problems, 2022, ECML-PKDD.[Paper]
  • GENERALIZATION IN DEEP RL FOR TSP PROBLEMS VIA EQUIVARIANCE AND LOCAL SEARCH, 2021, SN Computer Science.[Paper]
  • Learning 2‑Opt Heuristics for Routing Problems via Deep Reinforcement Learning, 2021, SN Computer Science.[Paper][Code]
  • 【LCP】Learning Collaborative Policies to Solve NP-hard Routing Problems, 2021, NeurIPS.[Paper][Code]
  • 【DACT】Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer, 2021, NeurIPS.[Paper][Code]
  • 【MDAM】Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems, 2020, AAAI.[Paper][Code]
  • 【POMO】POMO: Policy Optimization with Multiple Optima for Reinforcement Learning, 2020, NeurIPS.[Paper][Code]
  • 【AM】ATTENTION, LEARN TO SOLVE ROUTING PROBLEMS!, 2019, ICLR.[Paper][Code]

🐪Supervise Learning

  • 【LEHD】Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization, 2023, NeurIPS.[Paper][Code]
  • 【Att-GCRN】Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances, 2021, AAAI.[Paper][Code]
  • 【GCN】An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem, 2019, INFORMS.[Paper][Code]
  • 【Ptr-Net】Pointer Networks, 2017, NeurIPS.[Paper][Code]

🦙Unsupervised Learning

  • Unsupervised graph neural networks with recurrent features for solving combinatorial optimization problems, 2024, Preprint.[Paper]
  • 【UTSP】Unsupervised Learning for solving the TSP, 2023, NeurIPS.[Paper][Code]

🐄Self-Improvement Learning

  • 【GD】Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvement, 2024, TMLR.[Paper][Code]
  • 【SIL】Self-Improved Learning for Scalable Neural Combinatorial Optimization, 2024, Preprint.[Paper]
  • 【Step&Reconsider】Take a Step and Reconsider: Sequence Decoding for Self-Improved Neural Combinatorial Optimization, ECAI, 2024. [Paper][Code]
  • 【BQ-NCO】BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization, 2023, NeurIPS.[Paper][Code]
  • 【IIL】IMITATION IMPROVEMENT LEARNING FOR LARGESCALE CAPACITATED VEHICLE ROUTING PROBLEMS, 2023, ICAPS.[Paper][Code]

🐘Other

🐻‍❄️🐻‍❄️Large Language Models

  • 【EoH】Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model, 2024, ICML.[Paper][Code]
  • 【ReEvo】Large Language Models as Hyper-Heuristics for Combinatorial Optimization, 2024, Nips.[Paper][Code]
  • Prompt Learning for Generalized Vehicle Routing, 2024, IJCAI.[Paper][Code]

🐨🐨Duffusion model

  • 【DISCO】DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problems, 2024, Preprint.[Paper]
  • 【DIFUSCO】DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization, 2023, NeurIPS.[Paper][Code]

🐼🐼Knowledge Distillation

  • 【GNARKD】Distilling Autoregressive Models to Obtain High-Performance Non-Autoregressive Solvers for Vehicle Routing Problems with Faster Inference Speed, 2024, AAAI.[Paper][Code]
  • 【AMDKD】Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation, 2022, NeurIPS.[Paper][Code]

🐻🐻Meta-Learning

  • 【Meta-SAGE】Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization, 2023, ICML.[Paper][Code]
  • 【Omni-VRP】Towards Omni-generalizable Neural Methods for Vehicle Routing Problems, 2023, ICML.[Paper][Code]
  • 【DIMES】DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems, 2022, NeurIPS.[Paper][Code]