2024 Schedule:

• Learning to Solve Combinatorial Optimization Problems on Real-World Graphs in Linear Time, [paper], [GNN1], [GNN2]
  • 1/23, Aileen, [slides]
  • 1/30, Giovanni, [slides]
  • 2/8, Giovanni, [slides]
  • 2/15, Soham, [slides]
  
  
• 2/29,Vincent, Informative Path Planning for Mobile Sensing with Reinforcement Learning [paper1], [paper2], [slides]


• 3/7: John and Chris, Learn-as-you-go with Megh: Efficient Live Migration of Virtual Machines [paper1],[paper 2], [paper 3], [slides]


• 3/14: Giovanni, Learning combinatorial optimization algorithms over graphs, [paper], [slides], [codes]


• 3/21: Giovanni, Learning combinatorial optimization algorithms over graphs, [paper], [slides], [codes]

Related Resources of GNN

• Reinforcement Learning for Combinatorial Optimization: A Survey
• Learning combinatorial optimization on graphs: A survey with applications to networking
• How powerful are graph neural networks?, GitHub
• Stanford CS224W: Machine Learning with Graphs
• PyG: Graph Neural Networks Library

Related Resources of RL

• OpenAI's spinning up
• David Silver's course
• Berkeley deep RL
• Practical RL
• Deep Reinforcement Learning by Hung-yi Lee
• Deep-reinforcement-learning-with-pytorch
• Reinforcement Learning Algorithms
• Denny's Reinforcement Learning Github