Combinatorial Optimization ML Papers

• Predicting Propositional Satisfiability via End-to-End Learning
• Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching
• GLSearch: Maximum Common Subgraph Detection via Learning to Search
• Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers
• TAP-Net: Transport-and-Pack using Reinforcement Learning
• Learning deep graph matching with channel-independent embedding and Hungarian attention
• Learning Scheduling Algorithms for Data Processing Clusters
• A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing
• Combinatorial Learning of Graph Edit Distance via Dynamic Embedding
• Online 3D Bin Packing with Constrained Deep Reinforcement Learning
• Monte Carlo Tree Search on Perfect Rectangle Packing Problem Instances
• Learning Combinatorial Optimization Algorithms over Graphs
• Deep Graph Matching Consensus
• GLMNet: Graph Learning-Matching Networks for Feature Matching
• Differentiation of Blackbox Combinatorial Solvers
• Combinatorial Optimization Problems Related to Machine Learning Techniques
• A Learning-based Iterative Method for Solving Vehicle Routing Problems
• Robot Packing With Known Items and Nondeterministic Arrival Order
• PackIt: A Virtual Environment for Geometric Planning
• Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems
• Attention, Learn to Solve Routing Problems!
• Deep Reinforcement Learning of Graph Matching
• Deep Graphical Feature Learning for the Feature Matching Problem
• An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem
• Learning Improvement Heuristics for Solving Routing Problems
• Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
• Improving Learning to Branch via Reinforcement Learning
• Reversible Action Design for Combinatorial Optimization with Reinforcement Learning
• Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method
• Learning to Perform Local Rewriting for Combinatorial Optimization
• Graph Matching Networks for Learning the Similarity of Graph Structured Objects
• Learning Heuristics over Large Graphs via Deep Reinforcement Learning
• Solving Packing Problems by Conditional Query Learning
• SimGNN: A Neural Network Approach to Fast Graph Similarity Computation
• Smart Feasibility Pump: Reinforcement Learning for (Mixed) Integer Programming
• Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization
• Deep Learning based Hybrid Graph Coloring Algorithm for Register Allocation
• Learning for Graph Matching and Related Combinatorial Optimization Problems
• Exploratory Combinatorial Optimization with Reinforcement Learning
• Classification of SAT Problem Instances by Machine Learning Methods
• A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem
• POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
• Learning Combinatorial Embedding Networks for Deep Graph Matching
• Simultaneous Planning for Item Picking and Placing by Deep Reinforcement Learning
• Online Bayesian Moment Matching based SAT Solver Heuristics
• Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach
• Analytics and Machine Learning in Vehicle Routing Research
• G2SAT: Learning to Generate SAT Formulas
• Deep Reinforcement Learning for the Electric Vehicle Routing Problem with Time Windows
• Differentiable Learning of Submodular Models
• Small Boxes Big Data: A Deep Learning Approach to Optimize Variable Sized Bin Packing
• First-Order Problem Solving through Neural MCTS based Reinforcement Learning
• Deep Learning of Graph Matching
• Learning Local Search Heuristics for Boolean Satisfiability
• Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization
• Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search
• Guiding High-Performance SAT Solvers with Unsat-Core Predictions
• Resource Management with Deep Reinforcement Learning
• Causal Discovery with Reinforcement Learning
• Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?
• Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances
• Deep reinforcement learning-based dynamic scheduling in smart manufacturing
• MIPaaL: Mixed Integer Program as a Layer
• Machine Learning-based Restart Policy for CDCL SAT Solvers
• Distributed Scheduling using Graph Neural Networks
• Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning
• Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs
• Learning to Solve NP-Complete Problems: A Graph Neural Network for Decision TSP
• NLocalSAT: Boosting Local Search with Solution Prediction
• RP-DQN: An application of Q-Learning to Vehicle Routing Problems
• Graph Neural Networks and Boolean Satisfiability
• Enhancing SAT solvers with glue variable predictions
• Neural heuristics for SAT solving
• Expressing Combinatorial Optimization Problems by Linear Programs
• Interior Point Solving for LP-based prediction + optimisation
• Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement Learning
• Revised Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks
• Learning Clause Deletion Heuristics with Reinforcement Learning
• Graph Neural Reasoning May Fail in Certifying Boolean Unsatisfiability
• Multi-Agent Reinforcement Learning for Job Shop Scheduling in Flexible Manufacturing Systems
• Boosting Combinatorial Problem Modeling with Machine Learning
• The Transformer Network for the Traveling Salesman Problem